Unravelling Rieger and Quasi-Biennial Periodic Trends and Asymmetries in Coronal Mass Ejections Using Wavelet Analysis

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Minta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8603202/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this study, we investigate the periodic behavior of coronal mass ejections (CMEs) using statistical and wavelet analysis on SOHO/LASCO (1996–2024) and SEEDS (1996–2022) catalogs. We classified CMEs into joint angular width–speed (cluster I) and width–acceleration (cluster II), further separated into low- and high-latitude, northern and southern hemisphere subsets, and analyzed their relative occurrence rate, waiting times, and search for Rieger-type and quasi-biennial oscillations (QBOs) across solar cycles (SC) 23–24. We found that narrow CMEs occur during declining and minimum phases, while regular and partial/halo CMEs show consistency with SC activity. SEEDS shows a larger fraction of narrow CMEs (≈ 72%) than LASCO (≈ 45%), indicating catalog-dependent sensitivity to faint and small-scale eruptions. Furthermore, waiting times for narrow and regular CMEs are relatively longer (shorter) during the maximum (minimum) of SC23 (24), due to weaker polar fields and enhanced CME escape in the weaker cycle. Also, low-latitude CMEs dominate over high-latitude CMEs in both hemispheres, but the hemispheric asymmetry reverses between cycles. For instance, LASCO low-latitude CMEs show southern dominance in cycle 23 and northern dominance in cycle 24, whereas SEEDS exhibits opposite low-latitude dominance in cycle 23 but converges to northern dominance in cycle 24. Again, high-latitude CMEs display high northern dominance in SC24 with larger short-term variability and weaker correlation with sunspot number in both catalogs. Moreover, Rieger-type periods (≈ 3.7–6.4 months) and QBO signals at 1.3–2.73 year tend to repeat across specific latitudes and CME clusters showing identical ≈ 2.73 year periods in both catalogs, while Rieger-type periods weaken toward higher latitudes and are generally longer in SEEDS than in LASCO, with an anomalously long high-latitude southern Rieger period in cycle 24. Finally, Rieger-type periodicities decrease with increasing CME size in LASCO-decelerating CMEs, while QBO periodicities increase with CME size. Astrophysics and Cosmology Coronal Mass Ejections solar cycle Wavelet Analysis Waiting Times Periodicities Local and Global Wavelets Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Coronal mass ejections (CMEs) are macroscale explosive bursts of plasma and magnetic fields, travelling at speeds of hundreds to thousands of kilometers per second. Contingent on their starting speed, energy, and the solar cycle (SC) phase in which they occur, they typically reach the Earth within 1–4 days. During solar maximum, approximately five CMEs per day are launched from the Sun, whereas in solar minimum, a single CME occurs every five days (Minta et al. 2023 ; Srivastava et al. 2023 ). This generic link between solar activity and the frequency of CMEs suggests a wide variation in their physical characteristics, such as widths, speeds, accelerations, and latitude of origin. Establishing distinctive links between CMEs, strong solar flares, and geomagnetic disturbances requires the understanding of the evolutionary progression of CME features, and particularly any potential periodic patterns (Webb and Howard 2012 ). The Large Angle and Spectrometric Coronagraph (LASCO) suite onboard the Solar and Heliospheric Observatory (SOHO) (Brueckner et al. 1995 ; Domingo et al. 1995 ) has observed over 40000 CME events from 1996 to date, compiled manually through the Coordinated Data Analysis Workshop (CDAW) Initiative. Another variant of the CME database used in this study is the Solar Eruptive Event Detection System catalog (SEEDS) (from 1996 to the present) (Olmedo et al. 2008 ), which uses an automated threshold-segmentation technique to detect CMEs in polar-transformed running difference images derived from the LASCO-C2 coronagraph. These top-tier CME databases provide a robust and extensive data resource for authors to analyze CMEs in various dimensions, such as the hemispheric asymmetry (Zhang et al. 2023 ), latitudinal distribution (Gopalswamy et al. 2016 ; Zhang et al. 2023 ), and source regions (Yashiro and Gopalswamy 2008 ; Wang et al. 2011 ; Gao et al. 2014 ; Kim et al. 2017 ; Majumdar et al. 2023 ). It is worth noting that different CME categories exhibit distinct behaviors across SCs, resulting in significant discrepancies in their characteristics, distributions, and reliance on the photospheric magnetic fields (Bilenko 2020 ). Recently, Zhang et al. ( 2023 ) reported that CMEs originating from low-latitude active regions (ARs) are significantly associated with solar activity, while other studies also indicate that there are significant cycle-to-cycle discrepancies between the high-latitude and low-latitude originating CMEs in the northern and southern hemispheres. A substantial number of studies were conducted on the periodicities and characteristic behavior of CMEs across SC23 and 24 using different parameters and approaches. For instance, previous studies have identified mid-term periodicities such as Rieger-type and quasi-biennial oscillations (QBO) using parameters like occurrence rates (Li et al., 2023 ), angular widths (Zhang et al., 2023 ), and speeds (Ouyang et al., 2024 ). Nevertheless, significant deficiencies persist in comprehending CME periodic trends, asymmetries, waiting times, and their association with SC phases. Aside from the use of CME parameters, QBO signals have been identified in other solar indices, such as Hα flare activity with hemispheric phase asymmetries (Gyenge et al. 2016 ; Deng et al. 2017 ; Deng et al. 2019 ) and polar faculae displaying spatial distributions at high latitudes (Deng et al. 2020a ). These studies, together with QBOs in solar coronal rotation (Deng et al. 2020b ), demonstrate that QBO-scale modulation is a ubiquitous multi-layer phenomenon, motivating a dedicated assessment of their corresponding signatures in CME properties. Meanwhile, Li et al. ( 2023 ) show that CMEs display various periodicities based on their angular width and the hemisphere from which they originate. Their results indicated significant periodic signals at ≈ 6 months, 1.1 year, and 2.4 year, with clear differences observed between SC23 and SC24. Additionally, their wavelet and Fourier analyses revealed distinct periodicities that are present in only one hemisphere or specific SCs, emphasizing the intricate nature of CME occurrence patterns. Furthermore, Ouyang et al. ( 2024 ) also examined the periodicities of CMEs by analyzing their speed and acceleration. They identified distinct Rieger-type periodicities and QBOs showing differences in the periodic behaviors of CMEs between SC23 and SC24, with notable variations observed between the northern and southern hemispheres. They noted that fast CMEs displayed more complicated periodic structures compared to slow CMEs, indicating a strong correlation between CME speed and periodic behavior. Although these studies provide valuable insights, a more comprehensive assessment is needed, explicitly distinguishing between lower and higher latitudinal regions within each hemisphere. By integrating multiple CME properties, CME periodicities were analyzed using the wavelet technique through a unified framework of CME angular width-speed and angular width-acceleration classifications across segregated latitudes, examining the distribution of CME waiting times, and evaluating their response to different phases of SC23 and 24. Additionally, the study examines how these combined characteristics of CMEs respond to SC23 and 24, as understanding the correlations and interactions among these parameters is crucial for enhancing space weather forecasting and uncovering the underlying solar processes that drive CMEs. The rest of the paper is organized as follows: Section 2 contains a detailed description of the data processing methods and the wavelet analysis. The results and discussion are presented in section 3 , while section 4 summarizes the findings and concludes the study with implications, caveats, and recommendations for catalog selection based on parameters for future studies. 2. Data Sources, Preprocessing, and Wavelet Analysis 2.1. Data sources and preprocessing The present study leverages CME episodes contained in the SOHO/LASCO and SEEDS catalogs. Both catalogs span from January 1996 to till date. The SOHO/LASCO CME data repository, curated through human observation of CME running images, provides key parameters such as central position angle (CPA), mean position angle (MPA), angular width, linear speed, acceleration, and second-order speed at final height, etc. In contrast, SEEDS is an automated CME detection catalog (Olmedo et al., 2008 ). It applies a threshold-segmentation technique to detect CMEs from LASCO-C2 images, providing an independent dataset that complements the manually curated catalog. SEEDS has limited CME properties compared to its manual counterpart. While automatic catalogs like SEEDS, CACTus, and ARTEMIS have advantages in detection efficiency, they also have limitations compared to the SOHO/LASCO catalog, which leverages human expertise in identifying complex CME structures (Yashiro et al. 2008 ; Webb and Howard 2012 ). Despite their limitations, both SOHO/LASCO and SEEDS have remained the most used catalogs over the years. In this study, we extracted the speeds, angular widths, and accelerations of CME events from both catalogs for further analysis. It is worth noting that CME speed is one of the prominent parameters that describes the kinetic energy driven by magnetic reconnection in the corona, as reconnection facilitates 'the rapid conversion of this stored free magnetic energy into kinetic energy (see e.g., Forbes 2000 ; Lin and Forbes 2000 ). For instance, fast CMEs typically originate from ARs with strong magnetic fields (> 100 G), where complex topologies like flux ropes or sheared arcades release significant energy, given that fast CMEs are more often associated with AR involving high reconnection flux (e.g., Gopalswamy et al. 2017 ). In contrast, slow CMEs often arise from quiescent prominences with weaker fields (< 50 G), resulting in lower energy release. Thus, slow CMEs are heavily influenced by solar wind drag, while CMEs with speeds nearly equal to that of solar wind balance magnetic driving with ambient drag, and fast CMEs are associated with flare-related eruptions and interplanetary shocks. Zhao et al. ( 2017 ) noted that a CME's angular width is key in determining if the associated interplanetary CME and its preceding shock will impact Earth. Gopalswamy et al. ( 2002 ) and Kahler et al. ( 2001 ) earlier proposed that fast and wide CMEs are key contributors to large solar energetic particle (SEP) events, while certain impulsive SEP events are linked to fast but narrow CMEs. The acceleration of a CME is a manifestation of the Lorentz force, aerodynamic drag, and gravity. The Lorentz force primarily drives accelerating CMEs, while solar wind drag slows decelerating ones. Generally, slow- to intermediate-speed CMEs continue to accelerate, whereas fast CMEs tend to decelerate. Therefore, the relationship between these CME properties is a crucial metric for quantifying the anomalous expansion of CMEs that signals the diverse interaction between the heliosphere and CMEs (e.g. Dagnew et al. ( 2022 ); Gopalswamy et al. ( 2020 ). To investigate periodicities of CMEs with different speeds, accelerations, and widths, we first sort them into two clusters. Cluster I contains CMEs classified based on speed and angular widths, while cluster II comprises CMEs categorized based on angular width and acceleration. Specifically, cluster I CMEs are categorized according to the speed and angular criteria proposed by Yashiro et al. ( 2004 ), which include slow CMEs (V ≤ 250 km s⁻¹), intermediate-speed CMEs (250 km s⁻¹ < V ≤ 450 km s⁻¹), solar wind-speed CMEs (450 km s⁻¹ 900 km s⁻¹). The CMEs in this cluster are further grouped based on angular width: halo CMEs (angular width = 360°), partial-halo CMEs (120° < angular width < 360°), normal CMEs (20° < angular width ≤ 120°), and narrow CMEs (angular width ≤ 20°). In cluster II, CMEs are defined using the same angular width divisions but are further categorized as either accelerating (i.e., positive acceleration) or decelerating (i.e., negative acceleration) events. Thus, cluster I comprises CMEs classified by speed (slow, intermediate, solar-wind-speed, or fast) and angular width (narrow, halo, partial-halo, or normal), while cluster II consists of CMEs classified by angular width (narrow, halo, partial-halo, or normal) and acceleration behavior (accelerating or decelerating). In analyzing the hemispheric periodicities of CMEs, we convert the CPA of each CME into its projected heliographic latitude (apparent latitude) (e.g., Gopalswamy et al. 2003 ; Yashiro et al. 2004 ; Li et al. 2009 ; Gao et al. 2009 ). For instance, CPAs of 0°, 90°, 180°, and 270° correspond to apparent latitudes of 90°, 0°, − 90°, and 0°, respectively. Consequently, CMEs with apparent |latitude| ≤ 50° were classified as low-latitude CMEs, and those with |latitude| ≥ 60° as high-latitude CMEs, while CMEs with |latitude| between 50° and 60°, alongside their halo counterparts, were expunged to account for projection and magnetic-field mixing effects. To ensure data consistency, observation gaps from June 26 to October 9, 1998, and December 21, 1998, to February 2, 1999 (Gopalswamy et al. 2003 ) in these catalogs were handled using linear interpolation, while CMEs with undefined speeds, angular widths, and accelerations were expunged. The refined datasets (LASCO: January 1996- February 2024, SEEDS: January 1996 -May 2022), comprising a list of CME occurrence rates, angular widths, speed, and acceleration across time and latitudes, were used for the statistical and wavelets analyses described in the following subsection. 2.2. Periodicities: Wavelet power spectrum analysis Wavelet spectrum analysis is a widely used mathematical technique to examine the time-frequency variations and to identify shared periodic patterns between solar and geomagnetic activities. In this study, the processed CME time series is subjected to Continuous Wavelet Transform (CWT), with scales set logarithmically to capture a wide array of periodicities within the synchronized CME joint characteristic groups. The CWT analysis is conducted separately for each low- and high-latitude CME group in the northern and southern hemispheres, as well as CME angular width-speed and angular width-acceleration clusters, offering a detailed look into the hemispheric asymmetries over time. To achieve this, the Morlet wave function, combined with a bias correction technique and other parameters incorporated in the PyCWT v.0.4 package, was modified to obtain a desirable balance of the frequency and periodic signals of the CME events. The CWT, \(\:{W}_{n}^{x}\left(s\right)\) ) (Eq. 1 ), which is defined for the time series \(\:{x}_{n}\) where n = 1,…, N with uniform time steps \(\:dt\) , act as band-pass filter to the CME time series for extracting localize frequency information. $$\:{W}_{n}^{x}\left(s\right)=\:\sqrt{\frac{dt}{s}\:}\:{\sum\:}_{{n}^{{\prime\:}}=1}^{N}{x}_{{n}^{{\prime\:}}}{y}_{0}\left(\left({n}^{{\prime\:}}-\text{n}\right)\:\frac{dt}{s}\:\right)$$ 1 , where s is the wavelet scale and \(\:{y}_{0}\) denotes the Morlet wavelet defined as, $$\:{y}_{0}={p}^{-1/4}\:{e}^{-i{w}_{o}h}{e}^{-{h}^{2}/2}$$ 2 . Here, \(\:h\) is the nondimensional time and \(\:{w}_{o}\) representing the dimensionless center frequency was set to 6 to meet the eligibility criteria of providing equality between the period and the frequency localization (Grinsted et al. 2004 ). The Morlet wavelet, a complex sinusoidal function modulated by a Gaussian envelope, is particularly well-suited for capturing both frequency and temporal localization in the CME time series. This formulation allows for the identification of periodic signatures and transient features across a wide range of scales, providing a comprehensive analysis of CME dynamics. To guarantee physical consistency and eliminate bias among the wavelet transforms at each scale, as well as with the transforms of other time series, the energy of wavelet transforms at each scale is normalized to unity (e.g. (Torrence and Compo 1998 ): $$\:\widehat{\psi\:}\left(s{\omega\:}_{k}\right)=\:\sqrt{\frac{2\pi\:s}{dt}}\:{\widehat{\psi\:}}_{o}\left(s{\omega\:}_{k}\right)$$ 3 , where \(\:\widehat{\psi\:}\) \(\:\left(s\omega\:\right)\) is the Fourier transform and k = 0…N-1 denotes the frequency index. Since, the study deployed the convolution expression (Eq. 1 ), the normalized wavelet with energy equal to unity becomes, $$\:\psi\:\:\left[\frac{{(n}^{{\prime\:}}-n)dt}{s}\right]=\:\sqrt{\left(\frac{dt}{s}\right)}\:{y}_{0}\left[\frac{{(n}^{{\prime\:}}-n)dt}{s}\right]\:$$ 4 . In order to distinguish the peak signals from the background spectrum, the global wavelet power spectrum (GWS) is incorporated into the CWT as a benchmarking technique for assessing the peaks in the local wavelet spectrum. The GWS, which is the time-integrated square of the wavelet transforms, representing the mean variance (or mean power) contained in all wavelet coefficients at a specific scale, is expressed as, $$\:G\left(s\right)=\:\frac{{\sigma\:}_{{x}_{{n}^{{\prime\:}}}}^{2}}{T}\underset{o}{\overset{T}{\int\:}}{\:\left|{W}_{n}^{x}\left(s\right)\right|}^{2}dt\:\:$$ 5 , where \(\:{\sigma\:}_{x}^{2}\) denotes the variance of the CME time series, T signifies the duration, and \(\:{\left|{W}_{n}^{x}\left(s\right)\right|}^{2}\) represents the local wavelet power spectrum. Following previous methods (e.g. Torrence and Compo 1998 ; Grinsted et al. 2004 ; Li et al. 2023 ; Ouyang et al. 2024 ), the GWS was calculated by averaging the power within each frequency step, constrained by the cone of influence (COI) that restricts the time-frequency domain at 95% confidence level with red noise, α = 0.7. The enhancement of local power is deemed noteworthy when the power-to-red noise ratio exceeds unity at the 95% confidence level. In the visual representations of local wavelet spectra, areas where this condition (power/sig95 > 1) is satisfied are delineated by solid black contour lines. In principle, the GWS is used to identify the dominant periodic signals in the CME time series, and the statistical significance is evaluated using the red noise model (Torrence & Compo, 1998 ). It is worth noting that the COI and the 95% confidence level both play a role in determining the reliability of the results. The COI, marked by the white-shaded film (see Fig. 6 and subsequent ones) in the local wavelet spectrum, indicates where edge effects, which significantly distort the time series analysis, cannot be ignored (Grinsted et al. 2004 ), Torrence and Compo 1998 ). For this reason, peak signals within the COI must be considered as false. 3. Results and Discussion 3.1 Yearly statistical overview of angular-width CME classes Figure 1 shows the overall yearly frequencies and the fractional contributions of total CMEs together with the angular width classes (i.e., narrow, regular, partial, and halo) obtained from SOHO/LASCO and SEEDS data repositories. The top (bottom) left panels show the total annual frequencies of CMEs observed by SOHO/LASCO (SEEDS), overlaid with yearly counts of CMEs for each angular width class during SC23, 24, and 25. Similarly, the right panels contain color-coded histograms comprising the classes with their relative frequencies overlaid as lines. The histograms and the line plots in each panel are benchmarked with the sunspot numbers. Visual inspection of Fig. 1 (left panels) indicates that the yearly total counts of narrow, regular, partial, and halo CMEs (lines) in SOHO/LASCO demonstrate a strong correlation with solar cycles, while narrow and regular CMEs in SEEDS exhibit a parallel and more precise alignment with the sunspot number profile. Thus, the yearly total counts of CMEs in SEEDS are found to better track the trajectory of the sunspot number, particularly during solar minimum. This results from the cadence correction factor of 0.632 ± 0.047 applied by Hess and Colaninno ( 2017 ) to revise the CME occurrence rate in SEEDS. It is worth noting that the overall number of CMEs depicted by the histogram is significantly higher in the maximum phase of SC24, even though its SSN is ≈ 40 lower than that of SC23. This aligns with previous studies (e.g. Gopalswamy et al. 2014 ; Compagnino et al. 2017 ), which suggest that SC24’s weaker poloidal field and photospheric activity in SC24 facilitated the escape of more weaker CMEs into the heliosphere. This is supported by anomalous CME expansion in SC24, where ≈ 40% lower heliospheric total pressure (magnetic + plasma) enables faster radial broadening, enhancing detectability in white-light coronagraphs (see e.g., Gopalswamy et al. 2014 ). We argued that early SC25 points in Fig. 1 suggest a continuation of this pattern, with rising frequencies potentially overtaking SC24 if the cycle strengthens moderately. In contrast, the declining to minimum phase of SC23 shows marginal higher number of CMEs compared to the same phase of SC24. However, the opposite is true in SEEDS events. It is very possible that faint events in the deep minimum phase of SC24 may be overlooked in the process of compiling the CDAW catalog, while SEEDS' automation detects more during SC23 decline, possibly due to persistent high-latitude prominences erupting as narrow CMEs. Thus, during the declining phase, CMEs tend to be slower and narrower, making them more effectively detected by SEEDS' running-difference polar projections (see Figs. 1 and 2 ). It can be observed that CME frequencies symmetrically (and asymmetrically) correlate with SSN in phase (and amplitude) during SC23 and 24 for both SOHO/LASCO and SEEDS, consistent with findings by Gopalswamy et al. ( 2022 , 2020 ). Specifically, we observe that SC24's weaker amplitude reduces intense CMEs but increases total events due to expansion effects, while Gopalswamy et al. ( 2022 , 2020 ) also indicated that halo CME rates when normalized to SSN are higher in weaker cycles (e.g. SC24) due to reduced heliospheric pressure allowing more limb events to appear as halo events. This backreaction explains asymmetric amplitudes, with SC25 projections suggesting intermediate strength based on early halo abundance. The statistics show that SEEDS (SOHO/LASCO) recorded ≈ 72% (≈ 45%) of narrow CMEs arguably indicating that SEEDS is better at detecting faint small-scale CMEs that might harder for visual inspection. These findings are consistent with previous study by Olmedo et al. ( 2008 ), who established that SEEDS angular width measurements are typically narrower than those derived in SOHO/LASCO catalog since the algorithms for the former relies extensively on LASCO-C2 running images which is closer to the Sun (1.5–6 Rs), while the latter combines running images from both C2 and C3 (in outer corona: 3.7–30 Rs). In contrast, regular CME class is more frequent in SOHO/LASCO (47.01%) compared to SEEDS (27.29%), suggesting SEEDS might classify some events as narrow/partial CMEs. Although SEEDS does not explicitly define halo CMEs, partial CMEs (likely to contain halo events) contribute to ≈ 1% compared to the partial (≈ 6%) and halo (≈ 2%) in SOHO/LASCO. The shift in classification underscores methodological differences as LASCO’s human judgment may merge fragmented structures into regular/partial categories, while SEEDS' algorithm fragments wider events into narrower ones if brightness is uneven. Arguably, SEEDS prioritizes CME edge tracking over global geometry. Interestingly, significant variability in the narrow and regular CMEs is evident in both catalogs (see right panels), revealing asymmetric behavior across all phases of SC23 and SC24, particularly during the declining to minimum phases. Thus, the relative frequency of regular CMEs increases during the minimum to ascending phases and decreases during declining to minimum phases (e.g. SC23, 24), while the opposite is evident for the narrow CMEs. On the other hand, the relative frequencies of halo and partial CMEs in SOHO/LASCO symmetrically tracked the various phases of the solar cycles, albeit at significantly low relative percentages. 3. 2 Yearly statistical overview of CME speed classes In Fig. 2 , the angular width classes (Fig. 1 ) were replaced with the speed categories — slow (gold), intermediate (green), solar wind (blue), and fast CMEs (red). From Fig. 2 (left panels), the maximum phases of SC23 and SC24 exhibit asymmetries in CME speed groups — SC23 shows a higher yearly distribution of slow- and intermediate-speed CMEs, while SC24 is characterized by a lower contribution of fast and slow-speed CMEs. However, solar wind-speed CMEs are more pronounced in SC23 compared to SC24 in SOHO/LASCO, while fewer solar wind-speed CMEs are observed in SC24 relative to SC23 in SEEDS. One interpretation for this observation is that Alfvén speed in SC24's corona is ≈ 17% lower than that of SC23 (Gopalswamy et al. 2014 ; Kakad et al. 2019 ), hindering acceleration and making solar wind-speed CMEs (typically pseudo-streamers) less apparent from the background in LASCO. However, SEEDS' automation recorded them better via edge enhancement. Another noticeable observation is that higher frequencies of slow-speed CMEs are favored in SEEDS, while intermediate-speed CMEs are higher in LASCO during both cycles, highlighting discrepancies in the two CME databases. During the ascending-maximum phases (Fig. 2 , right panels), the relative percentages of slow (intermediate)-speed CMEs tend to decline (dominate), reflecting their asymmetrical (symmetrical) response to the gradual increase in solar activity. In the maximum phase, asymmetries in the relative contributions of speed categories are evident. It can be observed that SC23 shows a higher relative percentage of solar wind and intermediate-speed CMEs, while SC24 demonstrates a reduced contribution of fast, solar wind, slow-speed CMEs. This discrepancy is in good agreement with the weaker solar activity observed in SC24. A notable trend in SEEDS data reveals a consistent descending hierarchy in the relative percentages of CME speed groups across all solar cycles— slow-speed CMEs dominate, followed sequentially by intermediate, solar wind, and fast-speed CMEs, which contribute the least. As the SC enters the declining phase, the relative percentages of fast and solar wind-speed CMEs decrease, with slow- and intermediate-speed CMEs becoming more dominant. SC23 exhibits a more gradual decline in the relative contribution of fast CMEs compared to SC24, suggesting prolonged activity even after the solar maximum. This trend is less pronounced in SC24, which shows a sharper reduction in the relative percentages of fast and solar wind-speed CMEs during the declining phase. 3. 3 Waiting trends of speed- and angular-width CME classes In this subsection, the study attempts to examine the waiting time (i.e., time spans between consecutive events) trends of CMEs within each class during the period under consideration. The waiting-time distribution is an essential statistic to characterize the intermittency, or “ripple-effect” in the temporal process of CME formation, as well as instability growth occasioned by preceding CMEs (see e.g., Wang et al. 2013 ; Lamy et al. 2019 ). In this study, the waiting times were analyzed for CME groups obtained from both SOHO/LASCO and SEEDS to emphasize the long-term observational asymmetries. It is worth pointing out that Fig. 3 (upper and lower left panels) highlights drastic long-term asymmetries in the waiting times among the CME angular-width classes and the phases of solar cycles. While both datasets somewhat show periodic behavior of the waiting times linked to solar cycles, narrow (blue) and regular (orange) CMEs in SOHO/LASCO exhibit more pronounced oscillations between 2002 and 2012, with higher and lower waiting times asymmetrically associated across all phases of SC23 and SC24, respectively. Similar observations with relatively less amplitudes can be spotted in SEEDS CMEs (lower left), but with a-1 year lag in peak or trough (2007) when compared with LASCO (2008), highlighting discrepancies in observational cadence between these catalogs. This means that, in general, regular (narrow) CMEs follow preceding CMEs in shorter (relatively longer) times during solar maxima and vice-versa. Furthermore, the relatively lower CME waiting times in SC24 compared to SC23 indicate more and frequent CMEs escape into the heliosphere at short-time intervals. This reflects the presence of weak poloidal fields, which suggest reduced limitations on the strength of the closed global magnetic field during SC24, following the polar field reversal of SC23 (Petrie 2015 ; Michalek et al. 2019 ; Gopalswamy et al. 2020 ; Gopalswamy et al. 2022 ). While the waiting times for halo CMEs decrease gradually across the solar cycles, the waiting times for partial CME episodes remain consistently low in both SOHO/LASCO and SEEDS. The waiting times in the former exhibit synchronized periodic variations with the SC phases, whereas those in the latter follow a nearly linear trend. On the other hand, Fig. 3 (upper and lower right panels) compares the waiting-time distribution of CME speed groups in SOHO/LASCO and SEEDS. It can be observed that slow- and solar-wind type CMEs show asymmetries in their waiting times during all phases of SC23 and 24. Specifically, waiting times of the former (latter) decrease (increase) during the rising phase of SC23 and 24, while a mirrored effect of this behavior is also evident in the maximum phases. This means that slow (solar wind) type CMEs erupted earlier (relatively later) following preceding events during the maximum (minimum) phases of SC23 and 24, and vice-versa during the minimum phases. It is noteworthy that while the CME-speed clusters in SOHO/LASCO show higher amplitudes with a damping-effect in the solar wind class, SEEDS CME-speed classes have shorter waiting times (lower amplitudes), characterized by gradual undulating decline and increase, particularly slow- and solar-wind class CMEs, respectively. The intermediate class in SOHO/LASCO (SEEDS) shows relatively shorter waiting times with a marginal increase during SC23 and a steeper (gradual to nearly stable) decrease in the declining phase of SC24. Similarly, fast CME class shows significant variation in SOHO/LASCO compared to the nearly linear trends in SEEDS. In general, it can be suggested that a high CME occurrence rate during a particular SC (for instance, SC23), arguably, may lead to short waiting times for succeeding CMEs (see e.g., (Wang et al. 2013 ). 3. 4 Annual statistics of low and high latitude originating CMEs Figure 4 shows the yearly (left) and relative percentage contributions (right) of low- and high-latitude-originating CMEs during SC23, 24, and the rising phase of SC25. Low latitude events in northern and southern hemispheres are represented by dark-blue and deep blue, respectively, whereas high latitudes are shown in green and gold. In the left panels, it can be observed that SOHO/LASCO and SEEDS (in particular) clearly show that the distribution of both low- and high-latitude CMEs closely tracks the SC23 and SC24 activities in phase and not amplitude. Zhang et al. ( 2023 ) indicated that the occurrence rates of low-latitude regular CMEs are closely associated with solar activity. Although low-latitude CMEs from both catalogs are in phase with solar activity, their amplitudes are counter-correlated with that of SSN in SC23 and S24. Thus, the frequency of low-latitude CMEs is lower (higher) in SC23 (SC24). For high-latitude CMEs, their frequency is lower compared to the amplitude of SSN in SC23, but both show nearly the same amplitude in SC24. This observation is consistent with previous studies (Gopalswamy et al. 2003 ; Gopalswamy 2006 ; Zhang et al. 2023 ), which showed that high‐latitude CMEs are associated with polar‐crown filaments and are unrelated to SSN. This suggests that the CME occurrence rate is modulated not only by cycle strength but also by factors such as the underlying magnetic flux emergence or ambient coronal conditions, including lesser dependence on equatorial ARs and potentially greater influence from polar crown filaments or high-latitude streamers. In the right panels, which show the relative contributions of low and high-latitude CMEs as fractions of the total hemispheric activity, we observed that relative percentages of high-latitude CMEs in both hemispheres exhibit an in-phase relationship with SSN, while low-latitude fractions appear out of phase (i.e., declining at solar maxima and rising at minima), where they constitute a larger proportion of increased CME activity. The sharper decline in low-latitude CME fractions during the weaker maximum of SC24 potentially reflects the cycle's anomalously low magnetic field strength. A close look at the plots shows that while the low-latitude CMEs in both hemispheres are somewhat nearly the same contributions of high-latitude CMEs are quite different for both hemispheres. For instance, the frequencies and relative percentages of high-latitude CMEs in the northern hemisphere are relatively higher than their counterparts in the southern hemispheres especially in SC24. Similarly, there is a clear contrast between the relative percentage contribution of low-latitude CMEs in northern and southern hemispheres during solar minimum. This highlights the interplay of their hemispheric dominances and asymmetries, which are further assessed in the next subsection. In the high-latitude population, Zhang et al. ( 2023 ) found that northern and southern asymmetry is largely decoupled (e.g. the northern hemisphere dominated high-latitude CMEs in SC24, while the dominant hemisphere for low-latitude CMEs was southern in SC23 and switched to northern in SC24. Lamy et al. ( 2019 ) likewise found that SC24 produced far more northern CMEs than predicted by sunspot activity. 3.5 CME hemispheric asymmetry and dominance in relation to SSN between SOHO/LASCO and SEEDS catalogs To this end, we assess the asymmetric characteristic of CMEs in this present study for both catalogs using the normalized asymmetry index expression (e.g., Newton and Milsom 1955 ; Gao et al. 2009 ; Deng et al. 2016 ; Zhang et al. 2023 ) and attempt to explain the underlying mechanism that drives the north -south asymmetry of the CMEs. Figure 5 shows asymmetries of low-latitude (brown) and high-latitude (blue) CMEs and SSN (magenta) for SOHO/LASCO (panels a and b) and SEEDS CMEs (panels c and d). We tested the asymmetries for both yearly and monthly data of the events, and we realized that the latter gives a better representation of asymmetries, while the former appears to obscure the results; hence, we show here the monthly results of the analysis. During SC23, asymmetry index for low-latitude CMEs observed in SOHO/LASCO is largely negative around declining and minimum phases (2005–209) indicating a clear southern hemispheric dominance (SH 52%, NH 48%; see Fig. 5 a and Table 1 ) while the opposite hemisphere shows large dominance in SEEDS (NH 56.6%, SH 43.4%; Fig. 5 c). This switch from (to) northern (southern) hemisphere asymmetry before (after) SC23 maximum and the overall southern dominance (see Table 1 ) for SOHO/LASCO events is very consistent with findings by Gao et al. ( 2009 ), while Lamy et al. ( 2019 ) also observed the mirrored effect by SEEDS events reported in this study. For SC24, the asymmetry index in both catalogs shifts toward positive values during the cycle maximum (≈ 2012–2015, 2016–2020), implying a northern hemispheric dominance of low-latitude CMEs. These reversals of hemispheric activity of CMEs between SC23 and S24 indicate that strong asymmetry depends not just on instantaneous SSN but also on the long-term evolution of AR complexity, flux emergence patterns, and magnetic helicity injection in each hemisphere. Clearly, SSN in SC24 showed northern dominance (NH 60.6%, SH 39.4%), further supporting this link. For high-latitude CMEs, the asymmetry fluctuates rapidly between positive and negative values, with larger short-term variability and a weaker apparent correlation with the SSN curve (see Fig. 5 b and Table 1 ) during SC23, with intense northern hemispheric asymmetry in SC24. Similar asymmetric variability is observed in SEEDS CMEs; however, the correlation with SSN is relatively stronger than that of SOHO/LASCO CMEs (Fig. 5 d, Table 1 ). In SC24, correlations improve but remain moderate (SOHO/LASCO: ρ = 0.24, SEEDS: ρ = 0.43), with intense northern hemispheric asymmetry evident in both catalogs. Overall, across both cycles, low-latitude correlations with SSN are moderate (SOHO/LASCO: ρ = 0.34, SEEDS: ρ = 0.22), while high-latitude show variability (SOHO/LASCO: ρ = 0.20, SEEDS: ρ = 0.45) with oppositely related hemispheric dominance between the two catalogs. However, in general, the results show that SC23 is characterized by a mix of low-and high-latitude CME dominance, while SC24 exhibits a clear superiority in northern hemisphere dominance, consistent across both catalogs and align significant with results from earlier studies (see e.g. (Gao et al. 2009 ; Zhang et al. 2023 ; Zhang et al. 2024 ) It is worth mentioning that although north-south asymmetry may be considered as decoupled activities that needs sperate attentions (see e.g. (Lamy et al. 2019 ), the weak but significant correlation (coupling) unique to SC24 may be attributed to several factors: (1) The weaker polar magnetic fields in SC24 (Gopalswamy et al., 2016 ) may have reduced the magnetic barrier between polar and equatorial regions; (2) Altered meridional flow patterns during weaker cycles (Hathaway and Rightmire 2010 ) could transport magnetic flux differently; (3) The "rush-to-pole" phenomenon, where high- latitude magnetic features migrate rapidly toward the poles during cycle decline, may have been more pronounced in SC24, linking polar crown filament eruptions (source of high-latitude CMEs) with equatorial activity. Thus, northern hemisphere dominance for high-latitude CMEs in SC24 (for instance, 74.6% compared to 51.0% in SC23 in Table 1 ) further supports altered hemispheric coupling during solar cycles. Table 1 Summarized comparison of hemispheric asymmetry correlation between CMEs and SS for low-latitude and high-latitude events across SC23 and 24 using SOHO/LASCO and SEEDS catalogs. Shown are Spearman’s correlation coefficient with p-values and the corresponding percentages of northern (NH) and southern (SH) hemispheric dominance. Bold face values are statistically significant. SOHO/LASCO SEEDS Solar cycle Latitude ρ(p-value) CME dominance NH (SH) [%] ρ (p-value) CME dominance NH (SH) [%] SSN dominance NH (SH) [%] 23 Low 0.47 (< 0.001) 48 (52) 0.34 (< 0.001) 56.6 (43.4) 35.5 (64.5) 23 High 0.00 (0.98) 51 (49) 0.36 (< 0.001) 38.6 (61.4) — 24 Low 0.13 (0.14) 59.1 (40.9) 0.18 (0.037) 54.5 (45.5) 60.6 (39.4) 24 High 0.24 (0.005) 74.6 (25.4) 0.43 (< 0.001) 83.5 (16.5) — Overall Low 0.34 (< 0.001) 51.5 (48.5) 0.22 (< 0.001) 55.6 (44.4) 47.4 (52.6) Overall High 0.20 (< 0.001) 57.0 (43.0) 0.45 (< 0.001) 57.0 (43.0) — 3.6 Periodicities of low and high latitude originating CMEs Following the preceding results, we perform a comparative assessment of the periodic behavior of CMEs cataloged in LASCO and SEEDS through wavelet analysis, focusing on the following dimensions: (i) stratifying CME occurrence rates by low- and high-latitude origins in both the northern and southern hemispheres for SC23 and 24; (ii) grouping events into angular width—speed; and (iii) categorizing CME events based on width—acceleration/deceleration across these solar cycles. Thus, we seek to identify the Rieger-type oscillation (≈ 154 days) (Rieger et al. 1984 ) and QBO (0.6–4 year) (Bazilevskaya et al. 2014 ) for the CME clusters to assess the mechanisms that drive their underlying footprints from solar activity. It is worth noting that the cluster criteria adopted in this study extend and complement earlier studies that used CME speed—acceleration groups (Ouyang et al. 2024 ), angular width only (Li et al. 2023 ), and angular width—latitude classes (Wang et al. 2025 ). We searched for Rieger-type oscillations and QBO signals in low- and high-latitude originating CMEs during SC23 and SC24 (see Fig. 6 ), summarized in Table 2 . In each local wavelet spectrum, the amplitudes of wavelet power are represented by a color gradient, with red (green) signifying the lowest (highest) power. The periodicities extracted from the local and the GWS (inside COI) are shown individually in Table 2 , providing a useful summary of the findings that makes qualitative comparison simple. For both approaches, the error bar is calculated as half of the FWHM around a power peak. From Table 2 , the data reveal distinct differences in the two typical oscillations across latitude bands in the northern and southern hemispheres, as well as between SC23 and 24, when comparing the LASCO and SEEDS CMEs. From local to global within high and low-latitude CMEs across SC23, LASCO (SEEDS) CMEs show broad periodicities in Rieger and QBO signals on orders of 3.69 (2.88) months to 2.65 (2.73) yr., while in SC24 ranges typically vary between 2.87 (2.73) months and 2.78 (2.73) yr. However, the QBO periodicities, specifically, across stratified latitude bands in both hemispheres, exhibit remarkable consistency between LASCO and SEEDS CMEs, though SEEDS CMEs display relatively longer Rieger oscillations compared to LASCO. These values (in Table 2 ) align with prior studies (Barlyaeva et al. 2018 ; Lamy et al. 2019 ; Li et al. 2023 ; Ouyang et al. 2024 ; Wang et al. 2025 ) despite the absence of a clear explanation for the stable and reproducible patterns observed, as stated by (Wang and Sheeley, Jr. 2003). We observed several repeating CME estimated periodicities (regardless of uncertainties) across stratified latitudes within (and between) each catalog (LASCO and SEEDS), highlighted in bold face. For instance, we detect two instances of identical mid-term QBO periods at high-latitudes NH (1.37 year) and SH (2.73 year) for both SC23 and 24 in SEEDS CMEs. However, no identical Rieger periods were found in LASCO and SEEDS CMEs, consistent with observations by Li et al. ( 2023 ). Similarly, we identified matching QBO signals (≈ 2.28 year) in LASCO and SEEDS CMEs during SC24 for low and high-latitude NH and low-latitude SH. The discrepancies in these values emphasize the existence of hemispheric asymmetric signatures in CME distributions and source locations (see (Zhang et al. 2023 ; Zhang et al. 2024 ). Zhang et al., ( 2023 ) indicated that the cumulative number of low regular CMEs in the northern hemisphere consistently surpasses that in the southern hemisphere during SC24, reflecting a pronounced asymmetry in CME distribution between these cycles. Moreover, in SC23, Rieger-type oscillations (≈ 5.1 months) show distinct patterns with low-latitude NH exhibiting strong local (global) periods of ≈ 5.00 (4.42) months in LASCO and significantly longer periods in SEEDS (e.g., 6.3 months). During SC24, low-latitude NH shows longer local periods with LASCO at 5.8 months and SEEDS at 5.96 months, but the former maintains a strong signal at 5.58 months, while the latter shortens to 4.62 ± 0.06 months. This finding is consistent with Wang et al. ( 2025 ), who identified a significant periodicity of 6.23 months for low-latitude CMEs across SC23–25, suggesting that mid-term periodicities like the Rieger-type are prominent in low-latitude regions where magnetic activity is intense. As expected, in SC23, high-latitude SH SEEDS CMEs exhibited a notably weak periodic signal of 2.88 months, compared to a significantly longer period of 6.39 months during SC24. This unexpected anomaly in SC24 may reflect unique magnetic dynamics, potentially driven by “rush-to-pole effects’’ and the reversal of magnetic field polarity at high southern latitudes (see Gopalswamy et al. 2003 ; Gopalswamy et al. 2016 ). It is worth noting that the hemispheric asymmetry for shorter periodicities at high latitudes and a greater diversity at low-latitudes indicate that low-latitude CMEs from hemispheres are potentially influenced by differing magnetic dynamics from sunspots or ARs. Differences in CMEs’ periodicities at high and low latitudes are manifestations of genuine variations in the intensity of photospheric magnetic fields during SC23 and 24. It is noteworthy that global periodicities of CMEs (in LASCO and SEEDS) across SC23 and 24, spanning both hemispheres at high latitudes and local periodicities in SH at low latitudes, do not completely align with classical Rieger oscillations (≈ 155 days), which typically occur on timescales of 1–11 months. Instead, these periodicities, ranging from 118–199 days, correspond closely with planetary spring tides, potentially indicative of magneto-Rossby waves (see Zaqarashvili et al. 2010 ). The analysis reveals consistent short-term (e.g., 1.21, 1.29, 1.37 year) and mid-term (e.g., 2.3, 2.58, 2.73 year) QBO signals, which may also reflect the interplay of Rossby wave dynamics. These QBO signals, along with the scarcity of undetected periodicities (particularly at higher latitudes), provide evidence of magnetic Rossby wave instabilities, as noted by Bazilevskaya et al. ( 2014 ). Consequently, the estimated Rieger-type periodicities show a progressive weakening in high-latitude CMEs, while mid-term QBO signals remain persistent across both solar cycles. The presence of these instabilities in magneto-Rossby waves vis-à-vis Rieger-type and QB oscillations also highlights asymmetries between the northern and southern hemispheres. It is worth noting that multiple mechanisms have been proposed for Rieger-type periodicities and QBOs, including planetary tidal influences (Stefani et al. 2024 ), subsurface flow variations (Simoniello et al. 2013 ; Inceoglu et al. 2021 ), and stochastic fluctuations in Babcock–Leighton dynamo parameters (Kumar et al. 2025 ). Among these, global-scale magneto-Rossby waves arising from instabilities in the tachocline or dynamo layer (Zaqarashvili et al. 2010 ; Gurgenashvili et al. 2016 ) provide a unified framework, as the interaction of differential rotation with large-scale toroidal magnetic fields produces fast and slow branches whose dispersion relations yield both Rieger-type and QBO signals (> 2 year for stronger fields) (e.g., Zaqarashvili and Gurgenashvili 2018 ). This interpretation is consistent with our finding of coherent QBO bands (≈ 1.3–2.73 year) across catalogs and latitudes, particularly in low-latitude CMEs linked to active-region emergence, together with Rieger-type that weaken at high latitudes. Recent dynamo models further show that fluctuations in Babcock-Leighton parameters (e.g., poloidal field generation via flux emergence and tilt scatter) can produce Rieger-type and QBOs, with combined effects enhancing their occurrence (Kumar et al. 2025 ). However, isolated parameter variations may not fully replicate the observed hemispheric asymmetries in CMEs. This suggests that magneto-Rossby waves may act as an integrating mechanism, coupling dynamo variability with large-scale magnetic structuring. Given that CMEs represent large-scale magnetic eruptions, we argue that magneto-Rossby waves in the dynamo region provide the most physically plausible primary mechanism for the observed Rieger-type periodicities and QBOs, with other proposed mechanisms likely contributing as secondary effects. Table 2 List the periods with corresponding uncertainties for low and high-latitude CMEs in LASCO and SEEDs catalogs during SC23 and 24. Bold face highlights identical estimated periods regardless of uncertainties. Months = mon and years = yr. LASCO SEEDS CME group (wavelet type) Solar cycle Period Period 1– 11 mo 1–1.4 yr 1.5–2.8 yr 1–11 mo 1–1.4 yr 1.5–2.8 yr Northern hemisphere Low Latitude (Local) 23 5.00 ± 0.09 1.21 ± 0.11 2.07 ± 0.05 6.3 ± 0.07 1.13 ± 0.05 1.85 ± 0.11 24 5.8 ± 0.07 1.23 ± 0.08 2.00 ± 0.10 5.96 ± 0.08 1.28 ± 0.06 2.14 ± 0.07 Low Latitude (global) 23 4.42 ± 0.06 1.22 ± 0.12 — 5.28 ± 0.6 1.02 ± 0.1 1.82 ± 0.07 24 4.27 ± 0.09 — 2.28 ± 0.07 6.08 ± 0.05 — 2.28 ± 0.07 High Latitude (local) 23 5.12 ± 0.07 1.29 ± 0.05 2.16 ± 0.06 4.04 ± 0.1 1.37 ± 0.03 2.22 ± 0.11 24 5.77 ± 0.07 1.23 ± 0.08 2.00 ± 0.1 2.78 ± 0.1 1.37 ± 0.04 2.12 ± 0.08 High Latitude (global) 23 3.69 ± 0.08 1.29 ± 0.05 2.30 ± 0.04 4.43 ± 0.07 — 2.3 ± 0.08 24 2.87 ± 0.11 — 2.28 ± 0.63 3.8 ± 0.1 — 2.28 ± 0.04 Southern hemisphere Low Latitude (local) 23 5.4 ± 0.09 1.27 ± 0.01 2.65 ± 0.08 5.07 ± 0.09 1.13 ± 0.08 2.40 ± 0.06 24 5.58 ± 0.1 1.26 ± 0.1 2.1 ± 0.07 4.62 ± 0.06 1.12 ± 0.07 1.87 ± 0.08 Low Latitude (global) 23 4.01 ± 0.33 1.29 ± 0.01 — 3.78 ± 0.06 1.08 ± 0.08 2.58 ± 0.07 24 3.26 ± 0.06 — 2.28 ± 0.04 3.18 ± 0.08 1.02 ± 0.11 1.82 ± 0.07 High Latitude (local) 23 4.32 ± 0.08 1.25 ± 0.01 — 3.87 ± 0.08 1.32 ± 0.01 2.73 ± 0.01 24 5.18 ± 0.08 1.37 ± 0.00 2.73 ± 0.01 6.39 ± 0.11 — 2.50 ± 0.01 High Latitude (global) 23 4.43 ± 0.06 1.29 ± 0.01 2.30 ± 0.01 2.88 ± 0.07 1.32 ± 0.01 2.73 ± 0.01 24 4.88 ± 0.08 — 2.28 ± 0.01 3.84 ± 0.09 — 2.73 0.01 3.7 Periodicities of cluster I CMEs To gain a more complete insight into CME kinematics in the heliosphere, we now consider the angular width-speed profiles similar to the cluster in the preceding section. From Table 3 , it is evident that LASCO (Fig. 7 ) and SEEDS (Fig. 8 ) CME datasets exhibit identical QBO periods exclusively in the 1.5–2.8 year range for regular and partial CMEs. Particularly, LASCO and SEEDS share identical periods of 2.73 year for fast-regular, solar wind regular, intermediate-partial, and solar wind partial CMEs. Additionally, within the fast-partial CME category, LASCO global and local wavelets both show a period of 2.73 year, while in the slow-partial category, SEEDS global and local also converge at 2.73 year. These findings highlight a consistent 2.73 year QBO signal across both datasets for specific CME classes, with just a single identical Rieger period of 4.1 months observed within the 1–11 months and none in the 1–1.4 year ranges. In the narrow and regular CME categories across fast, intermediate, slow, and solar wind speed classes, SEEDS and LASCO exhibit broader range of Rieger-type periodicities; 3.68 to 7.38 months, and 2.3 to 6.3 months, respectively, although the former shows relatively longer Rieger activities compared to the latter, consistent with SEEDS’ high sensitivity and the tendency to detect smaller or slower events (Hess and Colaninno 2017 ). For example, fast narrow CMEs display a significant periodicity of ≈ 6.81 months (SEEDS local) compared to 5.4 months (LASCO local), while slow-regular CMEs have a duration of 5.46 months (SEEDS global) versus 3.97 months (LASCO global). Short-term QBO periodicities are limited in global wavelets, but consistent in local wavelets across both catalogs, appearing in LASCO local (≈ 1.05 − 1.26 year) and SEEDS local (≈ 1.03 − 1.35 year), underscoring findings from previous studies (e.g., Mursula et al. 2003 ; Knaack and Stenflo 2005 ; Knaack et al. 2005 ), suggesting that periodic oscillations of ≈ 1.3 year represent a unified phenomenon permeating multiple layers of solar and heliospheric activity. These processes manifest consistently across diverse domains, from the tachocline and photosphere evident in sunspot areas, counts, and large-scale magnetic fields to the Earth’s magnetosphere, influencing geomagnetic activity, and extending into the distant heliosphere, where it modulates cosmic ray variations. Similarly, in the 1.5–2.8 year range, a dominant QBO period of ≈ 2.73 year is evident in both datasets for regular fast and solar wind CMEs, aligning with the ≈ 2.44 year reported by Ouyang et al. ( 2024 ). For partial and halo CMEs across fast, intermediate, slow, and solar wind speed classes, Rieger-type periodicities (1–11 months) in SEEDS are generally longer for partial CME clusters (3.65–6.17 months) compared to LASCO (2.99 to 5.98 months), but SEEDS data lacks explicit definition for halo events, precluding direct comparisons. For instance, fast partial CMEs show 3.75 months (SEEDS) versus 2.99 months (LASCO), while LASCO fast halo CMEs exhibit 4.8 months (Global). Similarly, slow- and intermediate-partial CME clusters also show longer Rieger-type periodicities in SEEDS relative to their LASCO counterparts, except for the slow partial CME with 5.98 months. In general, there is a relatively higher scarcity of short and mid-term QBO activities in the global wavelet within the narrow clusters in SEEDS, regular and partial clusters in LASCO. However, the local wavelet shows dominance in both catalogs. Meanwhile, in this study, within the 1.5–2.8 year range, a consistent QBO periodicity of 2.73 year is observed across both datasets for intermediate partial and solar wind partial CMEs (in LASCO and SEEDS global wavelets). Additionally, SEEDS slow partial shows an intra-dataset match (global and local wavelets), confirming this periodicity as a stable feature of solar magnetic activity, consistently detected across different catalog methodologies. However, the variable periodic values aside the repeating 2.73 year highlights complexities and mix up behaviors in periodicities as direct consequences of variations in CME reported properties (e.g. speed and angular width), emanating from differences in detection algorithms and criteria between catalogs. The absence of an explicit definition of halo CME data in SEEDS may be linked to the following (i) the east-west asymmetry in SEP-associated CMEs (see Paouris et al. 2017 ), potentially affecting SEEDS’ ability to detect halo CMEs from certain solar regions, thus contributing to periodicity asymmetries. (ii) Halo CMEs appear as full-disk events in coronagraph images, making it difficult for automated algorithms to distinguish them from the background or other solar features. SEEDS relies on detecting bright ridges in running-difference images, which may not effectively capture the diffuse, all-encompassing nature of halo CMEs. (iii) Projection effect makes it very challenging to determine kinematical and geometrical properties of halo CMEs using single-spacecraft observations (i.e., SEEDS relies extensively on LASCO C2 running images) (Lamy et al. 2019 ). The analysis in this present study underscores significant asymmetries in the periodicities of CMEs, influenced by their origins in distinct solar source regions with varying kinematics, geometries, and magnetic properties. For instance, fast CMEs, associated with ARs harboring strong magnetic fields, exhibit periodicities that trail sunspot cycles, indicating a temporal asymmetry in their alignment with solar activity peaks (Gopalswamy et al. 2010 ). In contrast, slow CMEs, stemming from quiescent prominence eruptions in areas of weaker magnetic fields, show shorter (e.g., LASCO CMEs) or more irregular periodicity patterns in both LASCO and SEEDS, highlighting a contrasting temporal distribution compared to fast CMEs (Bilenko 2017 ). CMEs triggered by filament eruptions display pronounced asymmetry in their eruption speeds and periodicities, with speeds strongly correlated to the mean magnetic field strength in the filament channel. Non-active region filament CMEs exhibit higher speeds and distinct periodicity signatures compared to those from ARs (e.g., (Chen et al. 2006 ). Table 3 List the periods with corresponding uncertainties for CME width-speed clusters in LASCO and SEEDS catalogs. Bold face highlights identical estimated periods regardless of uncertainties. Months = mon and years = yr. LASCO SEEDS CME Class Speed Class Period Global Local Global Local Narrow Fast 1–11 mo 3.4 ± 0.01 5.4 ± 0.1 4.27 ± 0.07 6.81 ± 0.08 1–1.4 yr – 1.05 ± 0.02 – 1.16 ± 0.01 1.5–2.8 yr 2.06 ± 0.01 2.23 ± 0.01 – – Intermediate 1–11 mo 4.7 ± 0.05 4.8 ± 0.07 4.83 ± 0.05 5.77 ± 0.09 1–1.4 yr 1.08 ± 0.004 1.09 ± 0.005 – 1.26 ± 0.00 1.5–2.8 yr 1.82 ± 0.005 1.97 ± 0.006 – 2.40 ± 0.01 Slow 1–11 mo 4.1 ± 0.03 6.3 ± 0.06 3.68 ± 0.07 7.15 ± 0.08 1–1.4 yr – 1.19 ± 0.098 – 1.03 ± 0.00 1.5–2.8 yr 1.92 ± 0.006 2.00 ± 0.009 – 1.65 ± 0.00 Solar Wind 1–11 mo 2.3 ± 0.09 6.27 ± 0.07 4.12 ± 0.06 6.05 ± 0.08 1–1.4 yr – 1.17 ± 0.05 – 1.35 ± 0.01 1.5–2.8 yr 2.73 ± 0.009 2.72 ± 0.009 – 2.71 ± 0.01 Regular Fast 1–11 mo 4.57 ± 0.08 4.69 ± 0.1 4.97 ± 0.09 7.38 ± 0.08 1–1.4 yr – 1.21 ± 0.07 – – 1.5–2.8 yr 2.73 ± 0.009 2.72 ± 0.009 2.73 ± 0.02 2.64 ± 0.02 Intermediate 1–11 mo 3.77 ± 0.08 4.58 ± 0.09 4.55 ± 0.09 5.67 ± 0.09 1–1.4 yr – 1.26 ± 0.05 – – 1.5–2.8 yr – 2.15 ± 0.008 – 1.88 ± 0.01 Slow 1–11 mo 3.97 ± 0.06 4.67 ± 0.1 5.46 ± 0.07 6.31 ± 0.09 1–1.4 yr – 1.21 ± 0.06 – 1.10 ± 0.00 1.5–2.8 yr – 2.64 ± 0.014 – 1.68 ± 0.00 Solar Wind 1–11 mo 4.2 ± 0.06 5.44 ± 0.1 5.51 ± 0.07 5.26 ± 0.09 1–1.4 yr – 1.33 ± 0.03 – 1.35 ± 0.10 1.5–2.8 yr 2.73 ± 0.009 2.53 ± 0.008 2.73 ± 0.13 2.60 ± 0.14 Partial Fast 1–11 mo 2.99 ± 0.09 4.88 ± 0.09 3.75 ± 0.16 3.65 ± 0.10 1–1.4 yr – 1.07 ± 0.03 – – 1.5–2.8 yr 2.73 ± 0.009 2.73 ± 0.009 2.33 ± 0.01 – Intermediate 1–11 mo 3.08 ± 0.07 3.52 ± 0.1 5.11 ± 0.09 4.32 ± 0.08 1–1.4 yr 1.15 ± 0.04 1.18 ± 0.06 – 1.37 ± 0.01 1.5–2.8 yr 2.73 ± 0.012 2.55 ± 0.012 2.73 ± 0.13 2.69 ± 0.13 Slow 1–11 mo 4.1 ± 0.07 5.98 ± 0.09 4.51 ± 0.10 4.70 ± 0.08 1–1.4 yr – 1.17 ± 0.02 – – 1.5–2.8 yr – 2.58 ± 0.020 2.73 ± 0.16 2.73 ± 0.17 Solar Wind 1–11 mo 4.5 ± 0.04 4.65 ± 0.09 5.56 ± 0.08 6.17 ± 0.09 1–1.4 yr – 1.25 ± 0.07 – 1.30 ± 0.07 1.5–2.8 yr 2.73 ± 0.011 2.15 ± 0.008 2.73 ± 0.17 2.67 ± 0.17 Halo Fast 1–11 mo 4.8 ± 0.07 4.46 ± 0.09 – – 1–1.4 yr 1.37 ± 0.08 1.23 ± 0.09 – – 1.5–2.8 yr 2.58 ± 0.010 2.54 ± 0.009 – – Intermediate 1–11 mo 4.17 ± 0.09 4.54 ± 0.07 – – 1–1.4 yr – 1.22 ± 0.02 – – 1.5–2.8 yr – 2.57 ± 0.015 – – Slow 1–11 mo 5.00 ± 0.07 1.89 ± 0.1 – – 1–1.4 yr 1.15 ± 0.08 – – – 1.5–2.8 yr 2.17 ± 0.007 2.05 ± 0.001 – – Solar Wind 1–11 mo 2.99 ± 0.15 3.65 ± 0.09 – – 1–1.4 yr – 1.21 ± 0.1 – – 1.5–2.8 yr 2.73 ± 0.011 2.55 ± 0.12 – – 3.8 Periodicities of cluster II CMEs To understand how acceleration and angular width oscillations jointly influence the primary physical processes within CMEs, we analyzed the periodic behavior in the acceleration-angular width clusters across SC23 and 24, as shown in Fig. 9 . Comparatively, both LASCO and SEED CMEs reveal substantial differences in their local wavelet spectra, particularly during the maxima phases of SC23 and 24, while nearly similar patterns are observed at the minima. Similarly, the GWS in both datasets also exhibit different scaling behaviors. From Table 4 , it can be observed that LASCO and SEEDS CME episodes show identical QBO periods observed exclusively in the 1.5–2.8 year range for partial CMEs, with the global in the former and the latter both at 2.73 year for decelerating and accelerating classes, and local periods at 2.66 year for accelerating-partial CMEs. Similarly, (Ouyang et al. 2024 ) also found identical periodicities of 2.44 year in both accelerating and decelerating CMEs across SC23 and 24. Additionally, halo and partial decelerating CMEs also have identical periods of 1.22 and 2.73 year each, while their accelerating counterparts also maintained shared periodicities of 2.73 year. Similarly, narrow and regular decelerating classes display identical periods of 1.02 year (and the same value in the SEEDS narrow-accelerating group). Thus, these values reinforce the presence of shared mid-term periodicities in specific CME categories. On the contrary, there are no identical periods within LASCO or SEEDS CMEs themselves, indicating distinct spectral behaviors within each dataset despite some inter-dataset similarities in specific CME categories. For the decelerating category, narrow and regular CMEs in the SEEDS catalog show relatively longer Rieger periodicities, typically ranging between 5.20 and 6.30 months, compared to their LASCO counterparts (4.21–4.87) months, while decelerating-partial CMEs in SEEDS manifest the opposite, with reduced Rieger periods relative to LASCO CME events. It is worth mentioning that halo decelerating CMEs also show typical Rieger periodicity around 5.24 months. It can also be deduced that short-term QBO periodicities, for example, 1.02, 1.17, 1.22 year, etc., consistent with (Li et al. 2023 ; Ouyang et al. 2024 ; Wang et al. 2025 ), are prevalent across the clusters within the decelerating category, except that LASCO CMEs show more persistent/longer duration than their SEEDS counterparts. Thus, global QBO signals are absent in SEEDS CMEs. Notwithstanding the unexpected anomaly, we observed a striking anti-symmetric and undulating trend where Rieger durations decrease from narrow to regular and peak partial and halo CME events while QBO signals, in general, increase across the groups from narrow to halo events (i.e., increases as CME angular width increases). In the accelerating cluster, while Rieger-type periodicities are typically evident in each angular width class for LASCO and SEEDS, the latter exhibits relatively longer periodicities than the former. For instance, narrow and regular-accelerating CMEs have Rieger-type periodicities around 3.91– 5.17 months in LASCO and 4.98 − 6.37 months for SEEDS, while partial and halo display Rieger-type periodicities of 2.66–5.22 months for LASCO events and 4.7– 5.52 months. Just like the decelerating category, short-term QBO periodicities are more dominant than mid-term QBO signals across the angular width classes in the accelerating group. However, the scarcity of QBO activities further increases in the accelerating cluster, particularly for SEEDS CMEs. Arguably, the absence of global QBO signals in SEEDS indicates that its detection algorithm may miss certain large-scale periodic behaviors observed by LASCO. Again, Rieger-type periodicity duration decreases as angular width increases (narrow-halo), while QBO activities retain the increasing trend with increasing angular width, indicating a physical relationship between CME size and temporal dynamics driven by solar magnetic processes. Table 4 List the periods with corresponding uncertainties for CME width-acceleration clusters in LASCO and SEEDS catalogs. Bold face highlights identical estimated periods regardless of uncertainties. Months = mon and years = yr. Acceleration CME Class Period LASCO Global LASCO Local SEEDS Global SEEDS Local Decelerating Narrow 1–11 mo 4.52 ± 0.07 4.87 ± 0.07 5.53 ± 0.04 6.30 ± 0.09 1–1.4 yr 1.02 ± 0.01 1.03 ± 0.01 – 1.10 ± 0.00 1.5–2.8 yr 1.82 ± 0.01 1.95 ± 0.01 – 1.57 ± 0.00 Regular 1–11 mo 4.21 ± 0.07 4.58 ± 0.10 5.20 ± 0.06 5.97 ± 0.09 1–1.4 yr 1.02 ± 0.00 1.06 ± 0.01 – 1.08 ± 0.00 1.5–2.8 yr – 2.18 ± 0.09 – 1.63 ± 0.00 Partial 1–11 mo 4.41 ± 0.07 4.74 ± 0.09 4.56 ± 0.12 3.86 ± 0.08 1–1.4 yr 1.22 ± 0.00 1.17 ± 0.01 – 1.37 ± 0.01 1.5–2.8 yr 2.73 ± 0.11 2.23 ± 0.11 2.73 ± 0.14 2.56 ± 0.14 Halo 1–11 mo 5.24 ± 0.10 4.67 ± 0.09 – – 1–1.4 yr 1.22 ± 0.00 1.23 ± 0.01 – – 1.5–2.8 yr 2.73 ± 0.12 2.56 ± 0.12 – – Accelerating Narrow 1–11 mo 5.17 ± 0.05 5.05 ± 0.07 3.69 ± 0.04 6.37 ± 0.08 1–1.4 yr 1.08 ± 0.00 1.13 ± 0.01 – 1.02 ± 0.00 1.5–2.8 yr 1.72 ± 0.01 1.74 ± 0.01 – 1.55 ± 0.01 Regular 1–11 mo 3.91 ± 0.10 4.20 ± 0.08 4.98 ± 0.08 6.23 ± 0.08 1–1.4 yr – 1.31 ± 0.00 – – 1.5–2.8 yr – 2.48 ± 0.12 – 2.13 ± 0.10 Partial 1–11 mo 2.84 ± 0.07 5.02 ± 0.07 4.72 ± 0.08 5.52 ± 0.09 1–1.4 yr – 1.28 ± 0.00 – 1.37 ± 0.01 1.5–2.8 yr 2.73 ± 0.12 2.66 ± 0.11 2.73 ± 0.16 2.66 ± 0.15 Halo 1–11 mo 5.22 ± 0.07 4.89 ± 0.08 – – 1–1.4 yr 1.37 ± 0.01 1.31 ± 0.01 – – 1.5–2.8 yr 2.73 ± 0.11 2.66 ± 0.11 – – 4. Summary and Conclusion This paper presents a comprehensive statistical and wavelet analysis of coronal mass ejection (CME) properties using the manually curated SOHO/LASCO catalog (1996–2024) and the automated SEEDS catalog (1996–2022). We grouped CMEs into joint angular width–speed (cluster I) and width–acceleration (cluster II). Then, we further stratified the CMEs using their CPAs into low- and high-latitude events in the northern/southern hemispheres. We analyzed annual and relative occurrence rates, fractional contributions of width and speed classes, waiting-time distributions (inter-event intervals) for different classes, and hemispheric asymmetry indices correlated with sunspot numbers. Subsequently, we investigated the periodicities of the CMEs using continuous wavelet transform and global wavelet spectra to identify Rieger-type oscillations (≈ 3–11 months) and quasi-biennial oscillations (QBOs; ≈1–3 years) in occurrence rate time series across all latitude bands, hemispheres, and clusters during solar cycles 23 and 24. This multi-parameter framework enabled detailed examination of catalog-dependent sensitivities, solar-cycle modulations, hemispheric and latitudinal asymmetries, waiting-time variations, and the persistence or weakening of mid-term periodicities. Finally, we outlined the summarized key results from this present study below: Narrow CMEs accounted for a higher share in SEEDS (≈ 72%) than in LASCO (45%), while regular CMEs made up a smaller portion in SEEDS (27%) compared to LASCO (47%). In terms of overall CME frequency, the LASCO CMEs recorded a higher count during the maximum of SC24 than SC23. However, SEEDS CMEs show the same trend of higher frequencies in both SC23 and SC24. These substantially higher frequencies in SC24 are likely due to weaker poloidal magnetic fields allowing more weak CMEs to escape. Narrow CME relative frequencies are higher during declining/minimum phases of SC23–24, while regular CMEs dominate ascending/maxima. Halo and partial CMEs, though relatively less common, show significant correlation with solar activity. SC23 features elevated relative percentages of solar wind and intermediate-speed CMEs, while SC24 activity shows reduced contributions from fast, solar wind, and slow CMEs, consistent with weaker activity. In contrast, SEEDS consistently ranks slow CMEs as most frequent, followed by intermediate, solar wind, and fast CME types across SC23 and 24. Most CMEs originate at low latitudes, with high-latitude contributions peaking at SC maxima, but low-latitude CMEs show asynchronous variations to SC phases. hemispheric asymmetries reverse between cycles: LASCO low-latitude CMEs show southern dominance in SC23 (SH ≈ 52%) and northern in SC24 (NH ≈ 55–61%), while SEEDS exhibits northern in SC23 (NH ≈ 57%), but converges to northern in SC24 (NH ≈ 53–61%); high-latitude CMEs display strong northern dominance in SC24 (NH ≈ 65–75%) with larger short-term variability and weaker to moderate SSN correlations (ρ ≈ 0.2–0.45), potentially driven by weaker polar fields, altered meridional flows, and enhanced rush-to-pole effects linking polar/equatorial regions. Waiting times between consecutive CMEs reveal pronounced asymmetries tied to angular width and SC phases. Narrow CMEs occur less frequently (with longer waiting times) during solar maxima and more often (shorter waiting times) during solar minima, while regular CMEs show the opposite trend. CMEs in SC24 exhibit shorter waiting times than their SC23 counterparts, likely due to weaker post-reversal poloidal fields driving more frequent eruptions. Speed-based patterns add complexity: slow CMEs show reduced waiting times during the rising phase and increased waiting times during the maximum, while solar wind CMEs display the reverse behavior. In general, LASCO records larger waiting time variations than SEEDS. For certain CME classes, both the LASCO and SEEDS show a constant QBO short and mid-term periodicity of ≈ 1.3–2.73 year. This includes low- and high-latitude CMEs in the NH, as well as low-latitude CMEs in the SH during SC24, accelerating and decelerating partial and halo CMEs (both global and local), and different speed-width combinations, such as fast and solar wind regular CMEs. Rieger-type periodicities (≈ 3.7–6.4 months) are longer in SEEDS than LASCO, stronger at low latitudes (e.g., ≈ 5–6 months for NH low-latitude in SC23–24), and weaken at high latitudes, characterized by anomalously longer period in SEEDS SC24 high-latitude southern events. In principle, we find that Rieger-type periods decrease while QBOs increase with CME angular width (narrow to halo). Finally, we find significant differences in Rieger and QBO periods in hemispheres, latitudes, and solar cycles, with no identical Rieger periods between LASCO and SEEDS. Low-latitude CMEs display greater periodicity diversity, driven by AR magnetic dynamics, while high-latitude CMEs show shorter Rieger-type periodicities. QBO variations ≈ 1.3 year are common in local wavelets across both catalogs. CMEs in SOHO/LASCO repositories during SC23 exhibit broader periodic ranges than their counterparts in SEEDS. However, QBO periodicities remain consistent across latitude bands in both cycles. Implications and caveats for catalog selection It is worth emphasizing that SOHO/LASCO and SEEDS catalogs differ in detection methodology and event characterization, and therefore, their suitability depends strongly on the CME parameter under investigation. Nonetheless, based on our comparative analysis of the SOHO/LASCO and SEEDS catalogs across SC23 and 24, we attempt to provide the following recommendations with caveats for researchers selecting a catalog based on the scientific objectives under consideration: (i) For Studies of CME Occurrence Rate and SC Phase Association: SEEDS data is more suitable. Our findings show that SEEDS yearly counts, particularly for narrow and regular CMEs, track the sunspot number profile more precisely, especially during solar minimum. However, users should be aware of its cadence correction factor (≈0.63). SOHO/LASCO, on the other hand, may over-represent events during the maximum of weaker cycles (e.g., SC24) due to human identification of expansive and faint structures, which is useful for studying cycle-scale heliospheric pressure effects but may complicate straightforward occurrence rate studies. (ii) Angular Width-Dependent Studies: For studying narrow CMEs: SEEDS is superior, detecting ≈72% of events as narrow compared to ≈45% in LASCO. Its algorithm is more sensitive to faint, small-scale eruptions, making it ideal for investigating mini-CMEs or eruptions during solar minima. To study regular and partial/halo CMEs: SOHO/LASCO is more reliable. Human experts in the CDAW team are better at identifying the full spatial extent of wider, more complex CMEs and classifying halo events since these types are, in most cases, brighter and visible. SEEDS tends to fragment wider events or miss halos due to projection and background subtraction challenges. (iii) Speed and Kinematic Studies: For slow- and intermediate-speed CMEs: SEEDS recorded higher frequencies, suggesting its automated edge-tracking in running-difference images is effective for this class of events. However, to study solar wind-speed and fast CME types, as well as acceleration studies, the SOHO/LASCO catalog is recommended. The manual catalog maintains more consistent kinematics for events where precise leading-edge tracking is difficult for automation. Our analysis of periodicities in width-acceleration clusters (cluster II) was more definitive using LASCO data. (iv) Latitudinal and hemispheric Asymmetry Studies: SOHO/LASCO is preferred for investigating hemispheric asymmetries in source location. Its manual CPA determination, while subject to projection effects, provides a more consistent long-term record for latitudinal binning. The asynchronous behavior of low-latitude CMEs in LASCO reveals physically significant asymmetries tied to AR evolution. Researchers should note that SEEDS showed an anomalously long Rieger period for high-latitude SH CMEs in SC24, which may be a real physical signal or an artifact of its detection sensitivity at high latitudes during a weak cycle. Therefore, this requires cross-verification with the SOHO/LASCO catalog. (v) Periodicity and Wavelet Analysis: For Rieger-type periods (≈150 days): SEEDS tends to yield longer period estimates, likely due to its sensitivity to the repeated eruption of faint and narrow CMEs, which may have different drivers. LASCO periods are shorter and may be more representative of the dominant periodic forcing in ARs. For QBO periodicities: Both catalogs show remarkable consistency in QBO periods (e.g., with more ≈2.73-year frequent signals), especially for regular and partial CMEs. This suggests that for mid-term periodicity studies, either catalog is valid, and the QBO signal is a physical feature largely independent of detection methodology. (vi) For Waiting Time Analysis: SOHO/LASCO reveals larger amplitude variations and more pronounced solar-cycle modulation in waiting times, making it better for studying the interplay between CME eruption rhythms and global magnetic field evolution. In contrast, SEEDS waiting times are shorter and show less variation, useful for establishing a baseline "noise" floor or studying high-cadence eruption sequences. Finally, it should be noted that no single catalog is generally consistent; however, utilizing both catalogs simultaneously could provide an effective approach for thorough investigations because their differences alone might even reveal some underlying physical processes. Declarations Competing interests The author declares no competing interests. Acknowledgment Many thanks to all contributors of SOHO/LASCO, SEEDS, and WDC-SILSO, Royal Observatory of Belgium, Brussels, for making their catalog publicly available. The SOHO/LASCO catalog is part of the CDAW data center initiative by NASA and The Catholic University of America in cooperation with the Naval Research Laboratory. The SEEDS CME catalog is generated and maintained by the Space Weather Laboratory at George Mason University. Finally, the author extends his outmost gratitude to the reviewer (s) for his/her valuable comments in improving this manuscript Data Availability The SOHO/LASCO data can be accessed at https://cdaw.gsfc.nasa.gov/CME_list/ , SEEDS data available at http://spaceweather.gmu.edu/seeds/ , and the Sunspot number can be retrieved from https://www.sidc.be/SILSO/datafiles . References Barlyaeva T, Wojak J, Lamy P, Boclet B, Toth I (2018) Periodic behaviour of coronal mass ejections, eruptive events, and solar activity proxies during solar cycles 23 and 24. 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Monthly Notices of the Royal Astronomical Society 3936:3923–3936 Zhao XH, Feng XS, Feng HQ, Li Z (2017) Correlation between Angular Widths of CMEs and Characteristics of Their Source Regions. ApJ 849:79. https://doi.org/10.3847/1538-4357/aa8e49 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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05:03:10","extension":"html","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":267975,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8603202/v1/b5f045972b9310ee9ad1a1b7.html"},{"id":100548910,"identity":"ce04fd68-dcc2-46ab-b740-a0e01091fbfb","added_by":"auto","created_at":"2026-01-19 08:21:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1215289,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates the annual frequency of CMEs and CME angular width classes in LASCO (top panel) and SEEDS (bottom) extracted from the catalogs. The histograms on the right in both panels are color-coded to reflect the different speed categories they represent. The left y-axis indicates the number of events depicted by the histograms. The relative contribution of each category per year (compared to the total number of events for that year) is shown on the right y-axis, represented by the lines. The background highlights different phases of the solar cycle using varying shades of gray.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8603202/v1/7a2d15ed5a5be5ce62910908.jpeg"},{"id":100547965,"identity":"c6d36264-22a8-45d2-b7f6-ffc044d73877","added_by":"auto","created_at":"2026-01-19 08:17:09","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1260261,"visible":true,"origin":"","legend":"\u003cp\u003eShows the annual frequency of CMEs and CME-velocity classes in LASCO (top panel) and SEEDS (bottom) extracted from the catalogs. The histograms on the right in both panels are color-coded to reflect the different speed categories they represent. The left y-axis indicates the number of events depicted by the histograms. The relative contribution of each category per year (compared to the total number of events for that year) is shown on the right y-axis, represented by the lines. The background highlights different phases of the solar cycle using varying shades of gray.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8603202/v1/1c45fd55910cf585c22f1143.jpeg"},{"id":100498511,"identity":"dd00a39b-daef-47b1-8467-87366a696e9d","added_by":"auto","created_at":"2026-01-18 05:03:09","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":657836,"visible":true,"origin":"","legend":"\u003cp\u003eShows the normalized waiting time trends for CMEs clustered by angular width (left) and speed (right), from SOHO/LASCO (top panels) and SEEDS (bottom panels) observations. Different phases of the solar cycle are indicated with shaded regions.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8603202/v1/548a1966999714a501036fab.jpeg"},{"id":100549181,"identity":"83754f89-2388-41d3-b714-bbcf9cb6d949","added_by":"auto","created_at":"2026-01-19 08:22:42","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1168143,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates the annual frequency of CMEs and their hemispheric variations in LASCO (top panel) and SEEDS (bottom) extracted from the catalogs. The histograms on the right in both panels are color-coded to reflect the different latitude segments they represent. The left y-axis indicates the number of events depicted by the histograms. The relative hemispheric quantification of each category per year (compared to the total number of events for that year) is shown on the right y-axis, represented by the lines. The background highlights different phases of the solar cycle using varying shades of gray.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8603202/v1/7057ffff64de446b2c1388d8.jpeg"},{"id":100498516,"identity":"ec14f1b5-c6f7-45c0-b61f-73a941239a52","added_by":"auto","created_at":"2026-01-18 05:03:09","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":560690,"visible":true,"origin":"","legend":"\u003cp\u003eShows the asymmetry index for low-latitude (a) and high-latitude (b) CMEs from SOHO/LASCO catalog overlaid with the asymmetric profile of SSN. SC23, 24, and 25 are delineated in gray, peach, and teal colors, respectively. Same for panels c and d, but for CMEs listed in SEEDS catalog. Positive (negative) values indicate northern (southern) hemispheric dominance, which is quantified in Table 1.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8603202/v1/8f01366cf514b8118f2db091.jpeg"},{"id":100498530,"identity":"c1735e59-c235-4e14-9a64-0054aedf08b5","added_by":"auto","created_at":"2026-01-18 05:03:09","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1810414,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the local (columns 1,3) and global (columns 2, 4) wavelet spectra for LASCO and SEEDS CME occurrence rates stratified into low and high latitudes across SC23 and 24. In the local wavelet spectra, red (green) color gradients represent the lowest (highest) power and statistically significant power (95% confidence level) against the red-noise background, contoured with thick black lines. The white film regions represent the COI’s influence. In the GWS, the dashed red lines show the red-noise relative to the background at 95% significance level.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8603202/v1/fdb2a22e514c9d4a941a17e3.jpeg"},{"id":100548423,"identity":"f8a5157f-d448-4243-a3f7-d3c656bfada2","added_by":"auto","created_at":"2026-01-19 08:18:37","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1807393,"visible":true,"origin":"","legend":"\u003cp\u003eshows the local (columns 1,3) and global (columns 2, 4) wavelet spectra for LASCO (Jan 1996– Feb 2024) CME angular width–speed clusters. From the top, rows: (1–2: narrow–speed classes), (3–4: regular-speed classes), (5–6: partial-speed classes), and (7–8: halo-speed classes). Descriptions of the spectra are the same as in Figure 6.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8603202/v1/ce57756a10f9ee71e6a31aaf.jpeg"},{"id":100498546,"identity":"99dc68e3-5108-4a96-a2ef-ed905d32da0e","added_by":"auto","created_at":"2026-01-18 05:03:10","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2279665,"visible":true,"origin":"","legend":"\u003cp\u003erepresents the local (columns 1,3) and global (columns 2, 4) wavelet spectra for SEEDS (Jan 1996 – May 2022) CME angular width–speed clusters. From the top, rows: (1–2: narrow-speed classes), (3–4: regular-speed classes), and (5–6, partial speed classes). Descriptions of the spectra are the same as in Figure 6.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8603202/v1/56c414c6136d5b0d1ef4fb1c.png"},{"id":100498523,"identity":"e5470171-c107-4075-8c64-e708fd53e3a5","added_by":"auto","created_at":"2026-01-18 05:03:09","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1692133,"visible":true,"origin":"","legend":"\u003cp\u003eshows the local (columns 1,3) and global (columns 2, 4) wavelet spectra for LASCO (January 1996–Feb 2024) and SEEDS (January 1996–May 2022) CME angular width–acceleration clusters. First two columns (from the left) show the local and global wavelet spectra for the angular width-decelerating clusters, and the last two columns represent the width-acceleration clusters. Descriptions of the spectra are the same as in Figure 6.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8603202/v1/410370ed56c86a520ea84293.jpeg"},{"id":100594796,"identity":"46010cec-70b4-4860-afcb-cf812c51a99d","added_by":"auto","created_at":"2026-01-19 13:45:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14119093,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8603202/v1/0520f4a0-80e6-4d58-9f07-616588a48a0e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUnravelling Rieger and Quasi-Biennial Periodic Trends and Asymmetries in Coronal Mass Ejections Using Wavelet Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCoronal mass ejections (CMEs) are macroscale explosive bursts of plasma and magnetic fields, travelling at speeds of hundreds to thousands of kilometers per second. Contingent on their starting speed, energy, and the solar cycle (SC) phase in which they occur, they typically reach the Earth within 1\u0026ndash;4 days.\u003c/p\u003e \u003cp\u003eDuring solar maximum, approximately five CMEs per day are launched from the Sun, whereas in solar minimum, a single CME occurs every five days (Minta et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Srivastava et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This generic link between solar activity and the frequency of CMEs suggests a wide variation in their physical characteristics, such as widths, speeds, accelerations, and latitude of origin. Establishing distinctive links between CMEs, strong solar flares, and geomagnetic disturbances requires the understanding of the evolutionary progression of CME features, and particularly any potential periodic patterns (Webb and Howard \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Large Angle and Spectrometric Coronagraph (LASCO) suite onboard the Solar and Heliospheric Observatory (SOHO) (Brueckner et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Domingo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) has observed over 40000 CME events from 1996 to date, compiled manually through the Coordinated Data Analysis Workshop (CDAW) Initiative. Another variant of the CME database used in this study is the Solar Eruptive Event Detection System catalog (SEEDS) (from 1996 to the present) (Olmedo et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which uses an automated threshold-segmentation technique to detect CMEs in polar-transformed running difference images derived from the LASCO-C2 coronagraph. These top-tier CME databases provide a robust and extensive data resource for authors to analyze CMEs in various dimensions, such as the hemispheric asymmetry (Zhang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), latitudinal distribution (Gopalswamy et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and source regions (Yashiro and Gopalswamy \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kim et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Majumdar et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is worth noting that different CME categories exhibit distinct behaviors across SCs, resulting in significant discrepancies in their characteristics, distributions, and reliance on the photospheric magnetic fields (Bilenko \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recently, Zhang et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that CMEs originating from low-latitude active regions (ARs) are significantly associated with solar activity, while other studies also indicate that there are significant cycle-to-cycle discrepancies between the high-latitude and low-latitude originating CMEs in the northern and southern hemispheres. A substantial number of studies were conducted on the periodicities and characteristic behavior of CMEs across SC23 and 24 using different parameters and approaches. For instance, previous studies have identified mid-term periodicities such as Rieger-type and quasi-biennial oscillations (QBO) using parameters like occurrence rates (Li et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), angular widths (Zhang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and speeds (Ouyang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, significant deficiencies persist in comprehending CME periodic trends, asymmetries, waiting times, and their association with SC phases. Aside from the use of CME parameters, QBO signals have been identified in other solar indices, such as Hα flare activity with hemispheric phase asymmetries (Gyenge et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Deng et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Deng et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and polar faculae displaying spatial distributions at high latitudes (Deng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). These studies, together with QBOs in solar coronal rotation (Deng et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e), demonstrate that QBO-scale modulation is a ubiquitous multi-layer phenomenon, motivating a dedicated assessment of their corresponding signatures in CME properties.\u003c/p\u003e \u003cp\u003eMeanwhile, Li et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) show that CMEs display various periodicities based on their angular width and the hemisphere from which they originate. Their results indicated significant periodic signals at \u0026asymp;\u0026thinsp;6 months, 1.1\u0026nbsp;year, and 2.4\u0026nbsp;year, with clear differences observed between SC23 and SC24. Additionally, their wavelet and Fourier analyses revealed distinct periodicities that are present in only one hemisphere or specific SCs, emphasizing the intricate nature of CME occurrence patterns. Furthermore, Ouyang et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also examined the periodicities of CMEs by analyzing their speed and acceleration. They identified distinct Rieger-type periodicities and QBOs showing differences in the periodic behaviors of CMEs between SC23 and SC24, with notable variations observed between the northern and southern hemispheres. They noted that fast CMEs displayed more complicated periodic structures compared to slow CMEs, indicating a strong correlation between CME speed and periodic behavior.\u003c/p\u003e \u003cp\u003eAlthough these studies provide valuable insights, a more comprehensive assessment is needed, explicitly distinguishing between lower and higher latitudinal regions within each hemisphere. By integrating multiple CME properties, CME periodicities were analyzed using the wavelet technique through a unified framework of CME angular width-speed and angular width-acceleration classifications across segregated latitudes, examining the distribution of CME waiting times, and evaluating their response to different phases of SC23 and 24. Additionally, the study examines how these combined characteristics of CMEs respond to SC23 and 24, as understanding the correlations and interactions among these parameters is crucial for enhancing space weather forecasting and uncovering the underlying solar processes that drive CMEs. The rest of the paper is organized as follows: Section 2 contains a detailed description of the data processing methods and the wavelet analysis. The results and discussion are presented in section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3\u003c/span\u003e, while section 4 summarizes the findings and concludes the study with implications, caveats, and recommendations for catalog selection based on parameters for future studies.\u003c/p\u003e"},{"header":"2. Data Sources, Preprocessing, and Wavelet Analysis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. \u003cem\u003eData sources and preprocessing\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe present study leverages CME episodes contained in the SOHO/LASCO and SEEDS catalogs. Both catalogs span from January 1996 to till date. The SOHO/LASCO CME data repository, curated through human observation of CME running images, provides key parameters such as central position angle (CPA), mean position angle (MPA), angular width, linear speed, acceleration, and second-order speed at final height, etc. In contrast, SEEDS is an automated CME detection catalog (Olmedo et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). It applies a threshold-segmentation technique to detect CMEs from LASCO-C2 images, providing an independent dataset that complements the manually curated catalog. SEEDS has limited CME properties compared to its manual counterpart. While automatic catalogs like SEEDS, CACTus, and ARTEMIS have advantages in detection efficiency, they also have limitations compared to the SOHO/LASCO catalog, which leverages human expertise in identifying complex CME structures (Yashiro et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Webb and Howard \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Despite their limitations, both SOHO/LASCO and SEEDS have remained the most used catalogs over the years.\u003c/p\u003e \u003cp\u003eIn this study, we extracted the speeds, angular widths, and accelerations of CME events from both catalogs for further analysis. It is worth noting that CME speed is one of the prominent parameters that describes the kinetic energy driven by magnetic reconnection in the corona, as reconnection facilitates 'the rapid conversion of this stored free magnetic energy into kinetic energy (see e.g., Forbes \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Lin and Forbes \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). For instance, fast CMEs typically originate from ARs with strong magnetic fields (\u0026gt;\u0026thinsp;100 G), where complex topologies like flux ropes or sheared arcades release significant energy, given that fast CMEs are more often associated with AR involving high reconnection flux (e.g., Gopalswamy et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In contrast, slow CMEs often arise from quiescent prominences with weaker fields (\u0026lt;\u0026thinsp;50 G), resulting in lower energy release. Thus, slow CMEs are heavily influenced by solar wind drag, while CMEs with speeds nearly equal to that of solar wind balance magnetic driving with ambient drag, and fast CMEs are associated with flare-related eruptions and interplanetary shocks.\u003c/p\u003e \u003cp\u003eZhao et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) noted that a CME's angular width is key in determining if the associated interplanetary CME and its preceding shock will impact Earth. Gopalswamy et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and Kahler et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) earlier proposed that fast and wide CMEs are key contributors to large solar energetic particle (SEP) events, while certain impulsive SEP events are linked to fast but narrow CMEs. The acceleration of a CME is a manifestation of the Lorentz force, aerodynamic drag, and gravity. The Lorentz force primarily drives accelerating CMEs, while solar wind drag slows decelerating ones. Generally, slow- to intermediate-speed CMEs continue to accelerate, whereas fast CMEs tend to decelerate. Therefore, the relationship between these CME properties is a crucial metric for quantifying the anomalous expansion of CMEs that signals the diverse interaction between the heliosphere and CMEs (e.g. Dagnew et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Gopalswamy et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo investigate periodicities of CMEs with different speeds, accelerations, and widths, we first sort them into two clusters. Cluster I contains CMEs classified based on speed and angular widths, while cluster II comprises CMEs categorized based on angular width and acceleration. Specifically, cluster I CMEs are categorized according to the speed and angular criteria proposed by Yashiro et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), which include slow CMEs (V\u0026thinsp;\u0026le;\u0026thinsp;250 km s⁻\u0026sup1;), intermediate-speed CMEs (250 km s⁻\u0026sup1; \u0026lt; V\u0026thinsp;\u0026le;\u0026thinsp;450 km s⁻\u0026sup1;), solar wind-speed CMEs (450 km s⁻\u0026sup1; \u0026lt; V\u0026thinsp;\u0026le;\u0026thinsp;900 km s⁻\u0026sup1;), and fast CMEs (V\u0026thinsp;\u0026gt;\u0026thinsp;900 km s⁻\u0026sup1;). The CMEs in this cluster are further grouped based on angular width: halo CMEs (angular width\u0026thinsp;=\u0026thinsp;360\u0026deg;), partial-halo CMEs (120\u0026deg; \u0026lt; angular width\u0026thinsp;\u0026lt;\u0026thinsp;360\u0026deg;), normal CMEs (20\u0026deg; \u0026lt; angular width\u0026thinsp;\u0026le;\u0026thinsp;120\u0026deg;), and narrow CMEs (angular width\u0026thinsp;\u0026le;\u0026thinsp;20\u0026deg;). In cluster II, CMEs are defined using the same angular width divisions but are further categorized as either accelerating (i.e., positive acceleration) or decelerating (i.e., negative acceleration) events. Thus, cluster I comprises CMEs classified by speed (slow, intermediate, solar-wind-speed, or fast) and angular width (narrow, halo, partial-halo, or normal), while cluster II consists of CMEs classified by angular width (narrow, halo, partial-halo, or normal) and acceleration behavior (accelerating or decelerating).\u003c/p\u003e \u003cp\u003eIn analyzing the hemispheric periodicities of CMEs, we convert the CPA of each CME into its projected heliographic latitude (apparent latitude) (e.g., Gopalswamy et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Yashiro et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For instance, CPAs of 0\u0026deg;, 90\u0026deg;, 180\u0026deg;, and 270\u0026deg; correspond to apparent latitudes of 90\u0026deg;, 0\u0026deg;, \u0026minus;\u0026thinsp;90\u0026deg;, and 0\u0026deg;, respectively. Consequently, CMEs with apparent |latitude| \u0026le; 50\u0026deg; were classified as low-latitude CMEs, and those with |latitude| \u0026ge; 60\u0026deg; as high-latitude CMEs, while CMEs with |latitude| between 50\u0026deg; and 60\u0026deg;, alongside their halo counterparts, were expunged to account for projection and magnetic-field mixing effects.\u003c/p\u003e \u003cp\u003eTo ensure data consistency, observation gaps from June 26 to October 9, 1998, and December 21, 1998, to February 2, 1999 (Gopalswamy et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) in these catalogs were handled using linear interpolation, while CMEs with undefined speeds, angular widths, and accelerations were expunged. The refined datasets (LASCO: January 1996- February 2024, SEEDS: January 1996 -May 2022), comprising a list of CME occurrence rates, angular widths, speed, and acceleration across time and latitudes, were used for the statistical and wavelets analyses described in the following subsection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Periodicities: Wavelet power spectrum analysis\u003c/h2\u003e \u003cp\u003eWavelet spectrum analysis is a widely used mathematical technique to examine the time-frequency variations and to identify shared periodic patterns between solar and geomagnetic activities. In this study, the processed CME time series is subjected to Continuous Wavelet Transform (CWT), with scales set logarithmically to capture a wide array of periodicities within the synchronized CME joint characteristic groups. The CWT analysis is conducted separately for each low- and high-latitude CME group in the northern and southern hemispheres, as well as CME angular width-speed and angular width-acceleration clusters, offering a detailed look into the hemispheric asymmetries over time. To achieve this, the Morlet wave function, combined with a bias correction technique and other parameters incorporated in the PyCWT v.0.4 package, was modified to obtain a desirable balance of the frequency and periodic signals of the CME events. The CWT, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{n}^{x}\\left(s\\right)\\)\u003c/span\u003e\u003c/span\u003e) (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which is defined for the time series \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{n}\\)\u003c/span\u003e\u003c/span\u003e where n\u0026thinsp;=\u0026thinsp;1,\u0026hellip;, N with uniform time steps \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:dt\\)\u003c/span\u003e\u003c/span\u003e, act as band-pass filter to the CME time series for extracting localize frequency information.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{W}_{n}^{x}\\left(s\\right)=\\:\\sqrt{\\frac{dt}{s}\\:}\\:{\\sum\\:}_{{n}^{{\\prime\\:}}=1}^{N}{x}_{{n}^{{\\prime\\:}}}{y}_{0}\\left(\\left({n}^{{\\prime\\:}}-\\text{n}\\right)\\:\\frac{dt}{s}\\:\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere s is the wavelet scale and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{0}\\)\u003c/span\u003e\u003c/span\u003e denotes the Morlet wavelet defined as,\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{y}_{0}={p}^{-1/4}\\:{e}^{-i{w}_{o}h}{e}^{-{h}^{2}/2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e \u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:h\\)\u003c/span\u003e\u003c/span\u003e is the nondimensional time and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{o}\\)\u003c/span\u003e\u003c/span\u003e representing the dimensionless center frequency was set to 6 to meet the eligibility criteria of providing equality between the period and the frequency localization (Grinsted et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The Morlet wavelet, a complex sinusoidal function modulated by a Gaussian envelope, is particularly well-suited for capturing both frequency and temporal localization in the CME time series. This formulation allows for the identification of periodic signatures and transient features across a wide range of scales, providing a comprehensive analysis of CME dynamics. To guarantee physical consistency and eliminate bias among the wavelet transforms at each scale, as well as with the transforms of other time series, the energy of wavelet transforms at each scale is normalized to unity (e.g. (Torrence and Compo \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1998\u003c/span\u003e):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\widehat{\\psi\\:}\\left(s{\\omega\\:}_{k}\\right)=\\:\\sqrt{\\frac{2\\pi\\:s}{dt}}\\:{\\widehat{\\psi\\:}}_{o}\\left(s{\\omega\\:}_{k}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\widehat{\\psi\\:}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(s\\omega\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e is the Fourier transform and k\u0026thinsp;=\u0026thinsp;0\u0026hellip;N-1 denotes the frequency index. Since, the study deployed the convolution expression (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the normalized wavelet with energy equal to unity becomes,\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:\\psi\\:\\:\\left[\\frac{{(n}^{{\\prime\\:}}-n)dt}{s}\\right]=\\:\\sqrt{\\left(\\frac{dt}{s}\\right)}\\:{y}_{0}\\left[\\frac{{(n}^{{\\prime\\:}}-n)dt}{s}\\right]\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e \u003cp\u003eIn order to distinguish the peak signals from the background spectrum, the global wavelet power spectrum (GWS) is incorporated into the CWT as a benchmarking technique for assessing the peaks in the local wavelet spectrum. The GWS, which is the time-integrated square of the wavelet transforms, representing the mean variance (or mean power) contained in all wavelet coefficients at a specific scale, is expressed as,\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:G\\left(s\\right)=\\:\\frac{{\\sigma\\:}_{{x}_{{n}^{{\\prime\\:}}}}^{2}}{T}\\underset{o}{\\overset{T}{\\int\\:}}{\\:\\left|{W}_{n}^{x}\\left(s\\right)\\right|}^{2}dt\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{x}^{2}\\)\u003c/span\u003e\u003c/span\u003e denotes the variance of the CME time series, T signifies the duration, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\left|{W}_{n}^{x}\\left(s\\right)\\right|}^{2}\\)\u003c/span\u003e\u003c/span\u003e represents the local wavelet power spectrum.\u003c/p\u003e \u003cp\u003eFollowing previous methods (e.g. Torrence and Compo \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Grinsted et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ouyang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the GWS was calculated by averaging the power within each frequency step, constrained by the cone of influence (COI) that restricts the time-frequency domain at 95% confidence level with red noise, α\u0026thinsp;=\u0026thinsp;0.7. The enhancement of local power is deemed noteworthy when the power-to-red noise ratio exceeds unity at the 95% confidence level. In the visual representations of local wavelet spectra, areas where this condition (power/sig95\u0026thinsp;\u0026gt;\u0026thinsp;1) is satisfied are delineated by solid black contour lines. In principle, the GWS is used to identify the dominant periodic signals in the CME time series, and the statistical significance is evaluated using the red noise model (Torrence \u0026amp; Compo, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). It is worth noting that the COI and the 95% confidence level both play a role in determining the reliability of the results. The COI, marked by the white-shaded film (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and subsequent ones) in the local wavelet spectrum, indicates where edge effects, which significantly distort the time series analysis, cannot be ignored (Grinsted et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), Torrence and Compo \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). For this reason, peak signals within the COI must be considered as false.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 \u003cem\u003eYearly statistical overview of angular-width CME classes\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the overall yearly frequencies and the fractional contributions of total CMEs together with the angular width classes (i.e., narrow, regular, partial, and halo) obtained from SOHO/LASCO and SEEDS data repositories. The top (bottom) left panels show the total annual frequencies of CMEs observed by SOHO/LASCO (SEEDS), overlaid with yearly counts of CMEs for each angular width class during SC23, 24, and 25. Similarly, the right panels contain color-coded histograms comprising the classes with their relative frequencies overlaid as lines. The histograms and the line plots in each panel are benchmarked with the sunspot numbers. Visual inspection of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (left panels) indicates that the yearly total counts of narrow, regular, partial, and halo CMEs (lines) in SOHO/LASCO demonstrate a strong correlation with solar cycles, while narrow and regular CMEs in SEEDS exhibit a parallel and more precise alignment with the sunspot number profile. Thus, the yearly total counts of CMEs in SEEDS are found to better track the trajectory of the sunspot number, particularly during solar minimum. This results from the cadence correction factor of 0.632\u0026thinsp;\u0026plusmn;\u0026thinsp;0.047 applied by Hess and Colaninno (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to revise the CME occurrence rate in SEEDS. It is worth noting that the overall number of CMEs depicted by the histogram is significantly higher in the maximum phase of SC24, even though its SSN is \u0026asymp;\u0026thinsp;40 lower than that of SC23. This aligns with previous studies (e.g. Gopalswamy et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Compagnino et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which suggest that SC24\u0026rsquo;s weaker poloidal field and photospheric activity in SC24 facilitated the escape of more weaker CMEs into the heliosphere. This is supported by anomalous CME expansion in SC24, where \u0026asymp;\u0026thinsp;40% lower heliospheric total pressure (magnetic\u0026thinsp;+\u0026thinsp;plasma) enables faster radial broadening, enhancing detectability in white-light coronagraphs (see e.g., Gopalswamy et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). We argued that early SC25 points in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e suggest a continuation of this pattern, with rising frequencies potentially overtaking SC24 if the cycle strengthens moderately.\u003c/p\u003e \u003cp\u003eIn contrast, the declining to minimum phase of SC23 shows marginal higher number of CMEs compared to the same phase of SC24. However, the opposite is true in SEEDS events. It is very possible that faint events in the deep minimum phase of SC24 may be overlooked in the process of compiling the CDAW catalog, while SEEDS' automation detects more during SC23 decline, possibly due to persistent high-latitude prominences erupting as narrow CMEs. Thus, during the declining phase, CMEs tend to be slower and narrower, making them more effectively detected by SEEDS' running-difference polar projections (see Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It can be observed that CME frequencies symmetrically (and asymmetrically) correlate with SSN in phase (and amplitude) during SC23 and 24 for both SOHO/LASCO and SEEDS, consistent with findings by Gopalswamy et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specifically, we observe that SC24's weaker amplitude reduces intense CMEs but increases total events due to expansion effects, while Gopalswamy et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also indicated that halo CME rates when normalized to SSN are higher in weaker cycles (e.g. SC24) due to reduced heliospheric pressure allowing more limb events to appear as halo events. This backreaction explains asymmetric amplitudes, with SC25 projections suggesting intermediate strength based on early halo abundance.\u003c/p\u003e \u003cp\u003eThe statistics show that SEEDS (SOHO/LASCO) recorded\u0026thinsp;\u0026asymp;\u0026thinsp;72% (\u0026asymp;\u0026thinsp;45%) of narrow CMEs arguably indicating that SEEDS is better at detecting faint small-scale CMEs that might harder for visual inspection. These findings are consistent with previous study by Olmedo et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), who established that SEEDS angular width measurements are typically narrower than those derived in SOHO/LASCO catalog since the algorithms for the former relies extensively on LASCO-C2 running images which is closer to the Sun (1.5\u0026ndash;6 Rs), while the latter combines running images from both C2 and C3 (in outer corona: 3.7\u0026ndash;30 Rs).\u003c/p\u003e \u003cp\u003eIn contrast, regular CME class is more frequent in SOHO/LASCO (47.01%) compared to SEEDS (27.29%), suggesting SEEDS might classify some events as narrow/partial CMEs. Although SEEDS does not explicitly define halo CMEs, partial CMEs (likely to contain halo events) contribute to \u0026asymp;\u0026thinsp;1% compared to the partial (\u0026asymp;\u0026thinsp;6%) and halo (\u0026asymp;\u0026thinsp;2%) in SOHO/LASCO. The shift in classification underscores methodological differences as LASCO\u0026rsquo;s human judgment may merge fragmented structures into regular/partial categories, while SEEDS' algorithm fragments wider events into narrower ones if brightness is uneven. Arguably, SEEDS prioritizes CME edge tracking over global geometry. Interestingly, significant variability in the narrow and regular CMEs is evident in both catalogs (see right panels), revealing asymmetric behavior across all phases of SC23 and SC24, particularly during the declining to minimum phases. Thus, the relative frequency of regular CMEs increases during the minimum to ascending phases and decreases during declining to minimum phases (e.g. SC23, 24), while the opposite is evident for the narrow CMEs. On the other hand, the relative frequencies of halo and partial CMEs in SOHO/LASCO symmetrically tracked the various phases of the solar cycles, albeit at significantly low relative percentages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003cdiv class=\"Heading\"\u003e3. 2 \u003cem\u003eYearly statistical overview of CME speed classes\u003c/em\u003e\u003c/div\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the angular width classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were replaced with the speed categories \u0026mdash; slow (gold), intermediate (green), solar wind (blue), and fast CMEs (red). From Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (left panels), the maximum phases of SC23 and SC24 exhibit asymmetries in CME speed groups \u0026mdash; SC23 shows a higher yearly distribution of slow- and intermediate-speed CMEs, while SC24 is characterized by a lower contribution of fast and slow-speed CMEs. However, solar wind-speed CMEs are more pronounced in SC23 compared to SC24 in SOHO/LASCO, while fewer solar wind-speed CMEs are observed in SC24 relative to SC23 in SEEDS. One interpretation for this observation is that Alfv\u0026eacute;n speed in SC24's corona is \u0026asymp;\u0026thinsp;17% lower than that of SC23 (Gopalswamy et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kakad et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), hindering acceleration and making solar wind-speed CMEs (typically pseudo-streamers) less apparent from the background in LASCO. However, SEEDS' automation recorded them better via edge enhancement. Another noticeable observation is that higher frequencies of slow-speed CMEs are favored in SEEDS, while intermediate-speed CMEs are higher in LASCO during both cycles, highlighting discrepancies in the two CME databases.\u003c/p\u003e \u003cp\u003eDuring the ascending-maximum phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, right panels), the relative percentages of slow (intermediate)-speed CMEs tend to decline (dominate), reflecting their asymmetrical (symmetrical) response to the gradual increase in solar activity. In the maximum phase, asymmetries in the relative contributions of speed categories are evident. It can be observed that SC23 shows a higher relative percentage of solar wind and intermediate-speed CMEs, while SC24 demonstrates a reduced contribution of fast, solar wind, slow-speed CMEs. This discrepancy is in good agreement with the weaker solar activity observed in SC24. A notable trend in SEEDS data reveals a consistent descending hierarchy in the relative percentages of CME speed groups across all solar cycles\u0026mdash; slow-speed CMEs dominate, followed sequentially by intermediate, solar wind, and fast-speed CMEs, which contribute the least. As the SC enters the declining phase, the relative percentages of fast and solar wind-speed CMEs decrease, with slow- and intermediate-speed CMEs becoming more dominant. SC23 exhibits a more gradual decline in the relative contribution of fast CMEs compared to SC24, suggesting prolonged activity even after the solar maximum. This trend is less pronounced in SC24, which shows a sharper reduction in the relative percentages of fast and solar wind-speed CMEs during the declining phase.\u003c/p\u003e \n\u003cdiv class=\"Heading\"\u003e3. 3 \u003cem\u003eWaiting trends of speed- and angular-width CME classes\u003c/em\u003e\u003c/div\u003e \u003cp\u003eIn this subsection, the study attempts to examine the waiting time (i.e., time spans between consecutive events) trends of CMEs within each class during the period under consideration. The waiting-time distribution is an essential statistic to characterize the intermittency, or \u0026ldquo;ripple-effect\u0026rdquo; in the temporal process of CME formation, as well as instability growth occasioned by preceding CMEs (see e.g., Wang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lamy et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this study, the waiting times were analyzed for CME groups obtained from both SOHO/LASCO and SEEDS to emphasize the long-term observational asymmetries. It is worth pointing out that Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (upper and lower left panels) highlights drastic long-term asymmetries in the waiting times among the CME angular-width classes and the phases of solar cycles. While both datasets somewhat show periodic behavior of the waiting times linked to solar cycles, narrow (blue) and regular (orange) CMEs in SOHO/LASCO exhibit more pronounced oscillations between 2002 and 2012, with higher and lower waiting times asymmetrically associated across all phases of SC23 and SC24, respectively.\u003c/p\u003e \u003cp\u003eSimilar observations with relatively less amplitudes can be spotted in SEEDS CMEs (lower left), but with a-1 year lag in peak or trough (2007) when compared with LASCO (2008), highlighting discrepancies in observational cadence between these catalogs. This means that, in general, regular (narrow) CMEs follow preceding CMEs in shorter (relatively longer) times during solar maxima and vice-versa. Furthermore, the relatively lower CME waiting times in SC24 compared to SC23 indicate more and frequent CMEs escape into the heliosphere at short-time intervals. This reflects the presence of weak poloidal fields, which suggest reduced limitations on the strength of the closed global magnetic field during SC24, following the polar field reversal of SC23 (Petrie \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Michalek et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gopalswamy et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gopalswamy et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While the waiting times for halo CMEs decrease gradually across the solar cycles, the waiting times for partial CME episodes remain consistently low in both SOHO/LASCO and SEEDS. The waiting times in the former exhibit synchronized periodic variations with the SC phases, whereas those in the latter follow a nearly linear trend.\u003c/p\u003e \u003cp\u003eOn the other hand, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (upper and lower right panels) compares the waiting-time distribution of CME speed groups in SOHO/LASCO and SEEDS. It can be observed that slow- and solar-wind type CMEs show asymmetries in their waiting times during all phases of SC23 and 24. Specifically, waiting times of the former (latter) decrease (increase) during the rising phase of SC23 and 24, while a mirrored effect of this behavior is also evident in the maximum phases. This means that slow (solar wind) type CMEs erupted earlier (relatively later) following preceding events during the maximum (minimum) phases of SC23 and 24, and vice-versa during the minimum phases. It is noteworthy that while the CME-speed clusters in SOHO/LASCO show higher amplitudes with a damping-effect in the solar wind class, SEEDS CME-speed classes have shorter waiting times (lower amplitudes), characterized by gradual undulating decline and increase, particularly slow- and solar-wind class CMEs, respectively. The intermediate class in SOHO/LASCO (SEEDS) shows relatively shorter waiting times with a marginal increase during SC23 and a steeper (gradual to nearly stable) decrease in the declining phase of SC24. Similarly, fast CME class shows significant variation in SOHO/LASCO compared to the nearly linear trends in SEEDS. In general, it can be suggested that a high CME occurrence rate during a particular SC (for instance, SC23), arguably, may lead to short waiting times for succeeding CMEs (see e.g., (Wang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \n\u003cdiv class=\"Heading\"\u003e3. 4 \u003cem\u003eAnnual statistics of low and high latitude originating CMEs\u003c/em\u003e\u003c/div\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the yearly (left) and relative percentage contributions (right) of low- and high-latitude-originating CMEs during SC23, 24, and the rising phase of SC25. Low latitude events in northern and southern hemispheres are represented by dark-blue and deep blue, respectively, whereas high latitudes are shown in green and gold. In the left panels, it can be observed that SOHO/LASCO and SEEDS (in particular) clearly show that the distribution of both low- and high-latitude CMEs closely tracks the SC23 and SC24 activities in phase and not amplitude. Zhang et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicated that the occurrence rates of low-latitude regular CMEs are closely associated with solar activity. Although low-latitude CMEs from both catalogs are in phase with solar activity, their amplitudes are counter-correlated with that of SSN in SC23 and S24. Thus, the frequency of low-latitude CMEs is lower (higher) in SC23 (SC24). For high-latitude CMEs, their frequency is lower compared to the amplitude of SSN in SC23, but both show nearly the same amplitude in SC24. This observation is consistent with previous studies (Gopalswamy et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Gopalswamy \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which showed that high‐latitude CMEs are associated with polar‐crown filaments and are unrelated to SSN. This suggests that the CME occurrence rate is modulated not only by cycle strength but also by factors such as the underlying magnetic flux emergence or ambient coronal conditions, including lesser dependence on equatorial ARs and potentially greater influence from polar crown filaments or high-latitude streamers.\u003c/p\u003e \u003cp\u003eIn the right panels, which show the relative contributions of low and high-latitude CMEs as fractions of the total hemispheric activity, we observed that relative percentages of high-latitude CMEs in both hemispheres exhibit an in-phase relationship with SSN, while low-latitude fractions appear out of phase (i.e., declining at solar maxima and rising at minima), where they constitute a larger proportion of increased CME activity. The sharper decline in low-latitude CME fractions during the weaker maximum of SC24 potentially reflects the cycle's anomalously low magnetic field strength. A close look at the plots shows that while the low-latitude CMEs in both hemispheres are somewhat nearly the same contributions of high-latitude CMEs are quite different for both hemispheres. For instance, the frequencies and relative percentages of high-latitude CMEs in the northern hemisphere are relatively higher than their counterparts in the southern hemispheres especially in SC24. Similarly, there is a clear contrast between the relative percentage contribution of low-latitude CMEs in northern and southern hemispheres during solar minimum. This highlights the interplay of their hemispheric dominances and asymmetries, which are further assessed in the next subsection. In the high-latitude population, Zhang et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that northern and southern asymmetry is largely decoupled (e.g. the northern hemisphere dominated high-latitude CMEs in SC24, while the dominant hemisphere for low-latitude CMEs was southern in SC23 and switched to northern in SC24. Lamy et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) likewise found that SC24 produced far more northern CMEs than predicted by sunspot activity.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.5 CME hemispheric asymmetry and dominance in relation to SSN between SOHO/LASCO and SEEDS catalogs\u003c/h2\u003e \u003cp\u003eTo this end, we assess the asymmetric characteristic of CMEs in this present study for both catalogs using the normalized asymmetry index expression (e.g., Newton and Milsom \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1955\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Deng et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and attempt to explain the underlying mechanism that drives the north -south asymmetry of the CMEs. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows asymmetries of low-latitude (brown) and high-latitude (blue) CMEs and SSN (magenta) for SOHO/LASCO (panels a and b) and SEEDS CMEs (panels c and d). We tested the asymmetries for both yearly and monthly data of the events, and we realized that the latter gives a better representation of asymmetries, while the former appears to obscure the results; hence, we show here the monthly results of the analysis.\u003c/p\u003e \u003cp\u003eDuring SC23, asymmetry index for low-latitude CMEs observed in SOHO/LASCO is largely negative around declining and minimum phases (2005\u0026ndash;209) indicating a clear southern hemispheric dominance (SH 52%, NH 48%; see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) while the opposite hemisphere shows large dominance in SEEDS (NH 56.6%, SH 43.4%; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). This switch from (to) northern (southern) hemisphere asymmetry before (after) SC23 maximum and the overall southern dominance (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for SOHO/LASCO events is very consistent with findings by Gao et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), while Lamy et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) also observed the mirrored effect by SEEDS events reported in this study. For SC24, the asymmetry index in both catalogs shifts toward positive values during the cycle maximum (\u0026asymp;\u0026thinsp;2012\u0026ndash;2015, 2016\u0026ndash;2020), implying a northern hemispheric dominance of low-latitude CMEs. These reversals of hemispheric activity of CMEs between SC23 and S24 indicate that strong asymmetry depends not just on instantaneous SSN but also on the long-term evolution of AR complexity, flux emergence patterns, and magnetic helicity injection in each hemisphere. Clearly, SSN in SC24 showed northern dominance (NH 60.6%, SH 39.4%), further supporting this link.\u003c/p\u003e \u003cp\u003eFor high-latitude CMEs, the asymmetry fluctuates rapidly between positive and negative values, with larger short-term variability and a weaker apparent correlation with the SSN curve (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) during SC23, with intense northern hemispheric asymmetry in SC24. Similar asymmetric variability is observed in SEEDS CMEs; however, the correlation with SSN is relatively stronger than that of SOHO/LASCO CMEs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In SC24, correlations improve but remain moderate (SOHO/LASCO: ρ\u0026thinsp;=\u0026thinsp;0.24, SEEDS: ρ\u0026thinsp;=\u0026thinsp;0.43), with intense northern hemispheric asymmetry evident in both catalogs. Overall, across both cycles, low-latitude correlations with SSN are moderate (SOHO/LASCO: ρ\u0026thinsp;=\u0026thinsp;0.34, SEEDS: ρ\u0026thinsp;=\u0026thinsp;0.22), while high-latitude show variability (SOHO/LASCO: ρ\u0026thinsp;=\u0026thinsp;0.20, SEEDS: ρ\u0026thinsp;=\u0026thinsp;0.45) with oppositely related hemispheric dominance between the two catalogs. However, in general, the results show that SC23 is characterized by a mix of low-and high-latitude CME dominance, while SC24 exhibits a clear superiority in northern hemisphere dominance, consistent across both catalogs and align significant with results from earlier studies (see e.g. (Gao et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIt is worth mentioning that although north-south asymmetry may be considered as decoupled activities that needs sperate attentions (see e.g. (Lamy et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the weak but significant correlation (coupling) unique to SC24 may be attributed to several factors: (1) The weaker polar magnetic fields in SC24 (Gopalswamy et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) may have reduced the magnetic barrier between polar and equatorial regions; (2) Altered meridional flow patterns during weaker cycles (Hathaway and Rightmire \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) could transport magnetic flux differently; (3) The \"rush-to-pole\" phenomenon, where high- latitude magnetic features migrate rapidly toward the poles during cycle decline, may have been more pronounced in SC24, linking polar crown filament eruptions (source of high-latitude CMEs) with equatorial activity. Thus, northern hemisphere dominance for high-latitude CMEs in SC24 (for instance, 74.6% compared to 51.0% in SC23 in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) further supports altered hemispheric coupling during solar cycles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummarized comparison of hemispheric asymmetry correlation between CMEs and SS for low-latitude and high-latitude events across SC23 and 24 using SOHO/LASCO and SEEDS catalogs. Shown are Spearman\u0026rsquo;s correlation coefficient with p-values and the corresponding percentages of northern (NH) and southern (SH) hemispheric dominance. Bold face values are statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSOHO/LASCO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSEEDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolar cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ(p-value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCME dominance NH (SH) [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eρ (p-value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCME dominance NH (SH) [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSSN dominance NH (SH) [%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.47 (\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.34 (\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.6 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.5 (64.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00 (0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.36 (\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.6 (61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.1 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.18 (0.037)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.5 (45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.6 (39.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.24 (0.005)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.6 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.43 (\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.5 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.34 (\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.5 (48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.22 (\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.6 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.4 (52.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.20 (\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.0 (43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.45 (\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.0 (43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Periodicities of low and high latitude originating CMEs\u003c/h2\u003e \u003cp\u003eFollowing the preceding results, we perform a comparative assessment of the periodic behavior of CMEs cataloged in LASCO and SEEDS through wavelet analysis, focusing on the following dimensions: (i) stratifying CME occurrence rates by low- and high-latitude origins in both the northern and southern hemispheres for SC23 and 24; (ii) grouping events into angular width\u0026mdash;speed; and (iii) categorizing CME events based on width\u0026mdash;acceleration/deceleration across these solar cycles. Thus, we seek to identify the Rieger-type oscillation (\u0026asymp;\u0026thinsp;154 days) (Rieger et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) and QBO (0.6\u0026ndash;4\u0026nbsp;year) (Bazilevskaya et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) for the CME clusters to assess the mechanisms that drive their underlying footprints from solar activity. It is worth noting that the cluster criteria adopted in this study extend and complement earlier studies that used CME speed\u0026mdash;acceleration groups (Ouyang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), angular width only (Li et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and angular width\u0026mdash;latitude classes (Wang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe searched for Rieger-type oscillations and QBO signals in low- and high-latitude originating CMEs during SC23 and SC24 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In each local wavelet spectrum, the amplitudes of wavelet power are represented by a color gradient, with red (green) signifying the lowest (highest) power. The periodicities extracted from the local and the GWS (inside COI) are shown individually in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, providing a useful summary of the findings that makes qualitative comparison simple. For both approaches, the error bar is calculated as half of the FWHM around a power peak. From Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the data reveal distinct differences in the two typical oscillations across latitude bands in the northern and southern hemispheres, as well as between SC23 and 24, when comparing the LASCO and SEEDS CMEs. From local to global within high and low-latitude CMEs across SC23, LASCO (SEEDS) CMEs show broad periodicities in Rieger and QBO signals on orders of 3.69 (2.88) months to 2.65 (2.73) yr., while in SC24 ranges typically vary between 2.87 (2.73) months and 2.78 (2.73) yr. However, the QBO periodicities, specifically, across stratified latitude bands in both hemispheres, exhibit remarkable consistency between LASCO and SEEDS CMEs, though SEEDS CMEs display relatively longer Rieger oscillations compared to LASCO. These values (in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) align with prior studies (Barlyaeva et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lamy et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ouyang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) despite the absence of a clear explanation for the stable and reproducible patterns observed, as stated by (Wang and Sheeley, Jr. 2003). We observed several repeating CME estimated periodicities (regardless of uncertainties) across stratified latitudes within (and between) each catalog (LASCO and SEEDS), highlighted in bold face. For instance, we detect two instances of identical mid-term QBO periods at high-latitudes NH (1.37\u0026nbsp;year) and SH (2.73\u0026nbsp;year) for both SC23 and 24 in SEEDS CMEs. However, no identical Rieger periods were found in LASCO and SEEDS CMEs, consistent with observations by Li et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, we identified matching QBO signals (\u0026asymp;\u0026thinsp;2.28\u0026nbsp;year) in LASCO and SEEDS CMEs during SC24 for low and high-latitude NH and low-latitude SH. The discrepancies in these values emphasize the existence of hemispheric asymmetric signatures in CME distributions and source locations (see (Zhang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Zhang et al., (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicated that the cumulative number of low regular CMEs in the northern hemisphere consistently surpasses that in the southern hemisphere during SC24, reflecting a pronounced asymmetry in CME distribution between these cycles.\u003c/p\u003e \u003cp\u003eMoreover, in SC23, Rieger-type oscillations (\u0026asymp;\u0026thinsp;5.1 months) show distinct patterns with low-latitude NH exhibiting strong local (global) periods of \u0026asymp;\u0026thinsp;5.00 (4.42) months in LASCO and significantly longer periods in SEEDS (e.g., 6.3 months). During SC24, low-latitude NH shows longer local periods with LASCO at 5.8 months and SEEDS at 5.96 months, but the former maintains a strong signal at 5.58 months, while the latter shortens to 4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 months. This finding is consistent with Wang et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who identified a significant periodicity of 6.23 months for low-latitude CMEs across SC23\u0026ndash;25, suggesting that mid-term periodicities like the Rieger-type are prominent in low-latitude regions where magnetic activity is intense. As expected, in SC23, high-latitude SH SEEDS CMEs exhibited a notably weak periodic signal of 2.88 months, compared to a significantly longer period of 6.39 months during SC24. This unexpected anomaly in SC24 may reflect unique magnetic dynamics, potentially driven by \u0026ldquo;rush-to-pole effects\u0026rsquo;\u0026rsquo; and the reversal of magnetic field polarity at high southern latitudes (see Gopalswamy et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Gopalswamy et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It is worth noting that the hemispheric asymmetry for shorter periodicities at high latitudes and a greater diversity at low-latitudes indicate that low-latitude CMEs from hemispheres are potentially influenced by differing magnetic dynamics from sunspots or ARs. Differences in CMEs\u0026rsquo; periodicities at high and low latitudes are manifestations of genuine variations in the intensity of photospheric magnetic fields during SC23 and 24.\u003c/p\u003e \u003cp\u003eIt is noteworthy that global periodicities of CMEs (in LASCO and SEEDS) across SC23 and 24, spanning both hemispheres at high latitudes and local periodicities in SH at low latitudes, do not completely align with classical Rieger oscillations (\u0026asymp;\u0026thinsp;155 days), which typically occur on timescales of 1\u0026ndash;11 months. Instead, these periodicities, ranging from 118\u0026ndash;199 days, correspond closely with planetary spring tides, potentially indicative of magneto-Rossby waves (see Zaqarashvili et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The analysis reveals consistent short-term (e.g., 1.21, 1.29, 1.37\u0026nbsp;year) and mid-term (e.g., 2.3, 2.58, 2.73\u0026nbsp;year) QBO signals, which may also reflect the interplay of Rossby wave dynamics. These QBO signals, along with the scarcity of undetected periodicities (particularly at higher latitudes), provide evidence of magnetic Rossby wave instabilities, as noted by Bazilevskaya et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Consequently, the estimated Rieger-type periodicities show a progressive weakening in high-latitude CMEs, while mid-term QBO signals remain persistent across both solar cycles. The presence of these instabilities in magneto-Rossby waves vis-\u0026agrave;-vis Rieger-type and QB oscillations also highlights asymmetries between the northern and southern hemispheres.\u003c/p\u003e \u003cp\u003eIt is worth noting that multiple mechanisms have been proposed for Rieger-type periodicities and QBOs, including planetary tidal influences (Stefani et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), subsurface flow variations (Simoniello et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Inceoglu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and stochastic fluctuations in Babcock\u0026ndash;Leighton dynamo parameters (Kumar et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Among these, global-scale magneto-Rossby waves arising from instabilities in the tachocline or dynamo layer (Zaqarashvili et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gurgenashvili et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) provide a unified framework, as the interaction of differential rotation with large-scale toroidal magnetic fields produces fast and slow branches whose dispersion relations yield both Rieger-type and QBO signals (\u0026gt;\u0026thinsp;2\u0026nbsp;year for stronger fields) (e.g., Zaqarashvili and Gurgenashvili \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This interpretation is consistent with our finding of coherent QBO bands (\u0026asymp;\u0026thinsp;1.3\u0026ndash;2.73\u0026nbsp;year) across catalogs and latitudes, particularly in low-latitude CMEs linked to active-region emergence, together with Rieger-type that weaken at high latitudes.\u003c/p\u003e \u003cp\u003eRecent dynamo models further show that fluctuations in Babcock-Leighton parameters (e.g., poloidal field generation via flux emergence and tilt scatter) can produce Rieger-type and QBOs, with combined effects enhancing their occurrence (Kumar et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, isolated parameter variations may not fully replicate the observed hemispheric asymmetries in CMEs. This suggests that magneto-Rossby waves may act as an integrating mechanism, coupling dynamo variability with large-scale magnetic structuring. Given that CMEs represent large-scale magnetic eruptions, we argue that magneto-Rossby waves in the dynamo region provide the most physically plausible primary mechanism for the observed Rieger-type periodicities and QBOs, with other proposed mechanisms likely contributing as secondary effects.\u003c/p\u003e \u003cp\u003eTable 2 List the periods with corresponding uncertainties for low and high-latitude CMEs in LASCO and SEEDs catalogs during SC23 and 24. Bold face highlights identical estimated periods regardless of uncertainties. Months\u0026thinsp;=\u0026thinsp;mon and years\u0026thinsp;=\u0026thinsp;yr.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eLASCO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eSEEDS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCME group (wavelet type)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolar cycle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash; 11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNorthern hemisphere\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Latitude (Local)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Latitude (global)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Latitude (local)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Latitude (global)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSouthern hemisphere\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Latitude (local)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Latitude (global)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Latitude (local)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Latitude (global)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.73 \u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Periodicities of cluster I CMEs\u003c/h2\u003e \u003cp\u003eTo gain a more complete insight into CME kinematics in the heliosphere, we now consider the angular width-speed profiles similar to the cluster in the preceding section. From Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, it is evident that LASCO (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) and SEEDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) CME datasets exhibit identical QBO periods exclusively in the 1.5\u0026ndash;2.8\u0026nbsp;year range for regular and partial CMEs. Particularly, LASCO and SEEDS share identical periods of 2.73\u0026nbsp;year for fast-regular, solar wind regular, intermediate-partial, and solar wind partial CMEs. Additionally, within the fast-partial CME category, LASCO global and local wavelets both show a period of 2.73\u0026nbsp;year, while in the slow-partial category, SEEDS global and local also converge at 2.73\u0026nbsp;year. These findings highlight a consistent 2.73\u0026nbsp;year QBO signal across both datasets for specific CME classes, with just a single identical Rieger period of 4.1 months observed within the 1\u0026ndash;11 months and none in the 1\u0026ndash;1.4\u0026nbsp;year ranges.\u003c/p\u003e \u003cp\u003eIn the narrow and regular CME categories across fast, intermediate, slow, and solar wind speed classes, SEEDS and LASCO exhibit broader range of Rieger-type periodicities; 3.68 to 7.38 months, and 2.3 to 6.3 months, respectively, although the former shows relatively longer Rieger activities compared to the latter, consistent with SEEDS\u0026rsquo; high sensitivity and the tendency to detect smaller or slower events (Hess and Colaninno \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For example, fast narrow CMEs display a significant periodicity of \u0026asymp;\u0026thinsp;6.81 months (SEEDS local) compared to 5.4 months (LASCO local), while slow-regular CMEs have a duration of 5.46 months (SEEDS global) versus 3.97 months (LASCO global). Short-term QBO periodicities are limited in global wavelets, but consistent in local wavelets across both catalogs, appearing in LASCO local (\u0026asymp;\u0026thinsp;1.05 \u0026minus;\u0026thinsp;1.26\u0026nbsp;year) and SEEDS local (\u0026asymp;\u0026thinsp;1.03 \u0026minus;\u0026thinsp;1.35\u0026nbsp;year), underscoring findings from previous studies (e.g., Mursula et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Knaack and Stenflo \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Knaack et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), suggesting that periodic oscillations of \u0026asymp;\u0026thinsp;1.3\u0026nbsp;year represent a unified phenomenon permeating multiple layers of solar and heliospheric activity. These processes manifest consistently across diverse domains, from the tachocline and photosphere evident in sunspot areas, counts, and large-scale magnetic fields to the Earth\u0026rsquo;s magnetosphere, influencing geomagnetic activity, and extending into the distant heliosphere, where it modulates cosmic ray variations. Similarly, in the 1.5\u0026ndash;2.8\u0026nbsp;year range, a dominant QBO period of \u0026asymp;\u0026thinsp;2.73\u0026nbsp;year is evident in both datasets for regular fast and solar wind CMEs, aligning with the \u0026asymp;\u0026thinsp;2.44\u0026nbsp;year reported by Ouyang et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor partial and halo CMEs across fast, intermediate, slow, and solar wind speed classes, Rieger-type periodicities (1\u0026ndash;11 months) in SEEDS are generally longer for partial CME clusters (3.65\u0026ndash;6.17 months) compared to LASCO (2.99 to 5.98 months), but SEEDS data lacks explicit definition for halo events, precluding direct comparisons. For instance, fast partial CMEs show 3.75 months (SEEDS) versus 2.99 months (LASCO), while LASCO fast halo CMEs exhibit 4.8 months (Global). Similarly, slow- and intermediate-partial CME clusters also show longer Rieger-type periodicities in SEEDS relative to their LASCO counterparts, except for the slow partial CME with 5.98 months. In general, there is a relatively higher scarcity of short and mid-term QBO activities in the global wavelet within the narrow clusters in SEEDS, regular and partial clusters in LASCO. However, the local wavelet shows dominance in both catalogs. Meanwhile, in this study, within the 1.5\u0026ndash;2.8\u0026nbsp;year range, a consistent QBO periodicity of 2.73\u0026nbsp;year is observed across both datasets for intermediate partial and solar wind partial CMEs (in LASCO and SEEDS global wavelets). Additionally, SEEDS slow partial shows an intra-dataset match (global and local wavelets), confirming this periodicity as a stable feature of solar magnetic activity, consistently detected across different catalog methodologies. However, the variable periodic values aside the repeating 2.73\u0026nbsp;year highlights complexities and mix up behaviors in periodicities as direct consequences of variations in CME reported properties (e.g. speed and angular width), emanating from differences in detection algorithms and criteria between catalogs.\u003c/p\u003e \u003cp\u003eThe absence of an explicit definition of halo CME data in SEEDS may be linked to the following (i) the east-west asymmetry in SEP-associated CMEs (see Paouris et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), potentially affecting SEEDS\u0026rsquo; ability to detect halo CMEs from certain solar regions, thus contributing to periodicity asymmetries. (ii) Halo CMEs appear as full-disk events in coronagraph images, making it difficult for automated algorithms to distinguish them from the background or other solar features. SEEDS relies on detecting bright ridges in running-difference images, which may not effectively capture the diffuse, all-encompassing nature of halo CMEs. (iii) Projection effect makes it very challenging to determine kinematical and geometrical properties of halo CMEs using single-spacecraft observations (i.e., SEEDS relies extensively on LASCO C2 running images) (Lamy et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The analysis in this present study underscores significant asymmetries in the periodicities of CMEs, influenced by their origins in distinct solar source regions with varying kinematics, geometries, and magnetic properties. For instance, fast CMEs, associated with ARs harboring strong magnetic fields, exhibit periodicities that trail sunspot cycles, indicating a temporal asymmetry in their alignment with solar activity peaks (Gopalswamy et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In contrast, slow CMEs, stemming from quiescent prominence eruptions in areas of weaker magnetic fields, show shorter (e.g., LASCO CMEs) or more irregular periodicity patterns in both LASCO and SEEDS, highlighting a contrasting temporal distribution compared to fast CMEs (Bilenko \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). CMEs triggered by filament eruptions display pronounced asymmetry in their eruption speeds and periodicities, with speeds strongly correlated to the mean magnetic field strength in the filament channel. Non-active region filament CMEs exhibit higher speeds and distinct periodicity signatures compared to those from ARs (e.g., (Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList the periods with corresponding uncertainties for CME width-speed clusters in LASCO and SEEDS catalogs. Bold face highlights identical estimated periods regardless of uncertainties. Months\u0026thinsp;=\u0026thinsp;mon and years\u0026thinsp;=\u0026thinsp;yr.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eLASCO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eSEEDS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCME Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpeed Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNarrow\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolar Wind\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegular\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolar Wind\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePartial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolar Wind\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHalo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolar Wind\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Periodicities of cluster II CMEs\u003c/h2\u003e \u003cp\u003eTo understand how acceleration and angular width oscillations jointly influence the primary physical processes within CMEs, we analyzed the periodic behavior in the acceleration-angular width clusters across SC23 and 24, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. Comparatively, both LASCO and SEED CMEs reveal substantial differences in their local wavelet spectra, particularly during the maxima phases of SC23 and 24, while nearly similar patterns are observed at the minima. Similarly, the GWS in both datasets also exhibit different scaling behaviors. From Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it can be observed that LASCO and SEEDS CME episodes show identical QBO periods observed exclusively in the 1.5\u0026ndash;2.8\u0026nbsp;year range for partial CMEs, with the global in the former and the latter both at 2.73\u0026nbsp;year for decelerating and accelerating classes, and local periods at 2.66\u0026nbsp;year for accelerating-partial CMEs. Similarly, (Ouyang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also found identical periodicities of 2.44\u0026nbsp;year in both accelerating and decelerating CMEs across SC23 and 24. Additionally, halo and partial decelerating CMEs also have identical periods of 1.22 and 2.73\u0026nbsp;year each, while their accelerating counterparts also maintained shared periodicities of 2.73\u0026nbsp;year. Similarly, narrow and regular decelerating classes display identical periods of 1.02\u0026nbsp;year (and the same value in the SEEDS narrow-accelerating group). Thus, these values reinforce the presence of shared mid-term periodicities in specific CME categories. On the contrary, there are no identical periods within LASCO or SEEDS CMEs themselves, indicating distinct spectral behaviors within each dataset despite some inter-dataset similarities in specific CME categories.\u003c/p\u003e \u003cp\u003eFor the decelerating category, narrow and regular CMEs in the SEEDS catalog show relatively longer Rieger periodicities, typically ranging between 5.20 and 6.30 months, compared to their LASCO counterparts (4.21\u0026ndash;4.87) months, while decelerating-partial CMEs in SEEDS manifest the opposite, with reduced Rieger periods relative to LASCO CME events. It is worth mentioning that halo decelerating CMEs also show typical Rieger periodicity around 5.24 months. It can also be deduced that short-term QBO periodicities, for example, 1.02, 1.17, 1.22\u0026nbsp;year, etc., consistent with (Li et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ouyang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), are prevalent across the clusters within the decelerating category, except that LASCO CMEs show more persistent/longer duration than their SEEDS counterparts. Thus, global QBO signals are absent in SEEDS CMEs. Notwithstanding the unexpected anomaly, we observed a striking anti-symmetric and undulating trend where Rieger durations decrease from narrow to regular and peak partial and halo CME events while QBO signals, in general, increase across the groups from narrow to halo events (i.e., increases as CME angular width increases).\u003c/p\u003e \u003cp\u003eIn the accelerating cluster, while Rieger-type periodicities are typically evident in each angular width class for LASCO and SEEDS, the latter exhibits relatively longer periodicities than the former. For instance, narrow and regular-accelerating CMEs have Rieger-type periodicities around 3.91\u0026ndash; 5.17 months in LASCO and 4.98 \u0026minus;\u0026thinsp;6.37 months for SEEDS, while partial and halo display Rieger-type periodicities of 2.66\u0026ndash;5.22 months for LASCO events and 4.7\u0026ndash; 5.52 months. Just like the decelerating category, short-term QBO periodicities are more dominant than mid-term QBO signals across the angular width classes in the accelerating group. However, the scarcity of QBO activities further increases in the accelerating cluster, particularly for SEEDS CMEs. Arguably, the absence of global QBO signals in SEEDS indicates that its detection algorithm may miss certain large-scale periodic behaviors observed by LASCO. Again, Rieger-type periodicity duration decreases as angular width increases (narrow-halo), while QBO activities retain the increasing trend with increasing angular width, indicating a physical relationship between CME size and temporal dynamics driven by solar magnetic processes.\u003c/p\u003e \u003cp\u003eTable 4 List the periods with corresponding uncertainties for CME width-acceleration clusters in LASCO and SEEDS catalogs. Bold face highlights identical estimated periods regardless of uncertainties. Months\u0026thinsp;=\u0026thinsp;mon and years\u0026thinsp;=\u0026thinsp;yr.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcceleration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCME Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLASCO Global\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLASCO Local\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSEEDS Global\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSEEDS Local\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDecelerating\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNarrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHalo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAccelerating\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNarrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHalo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;11 mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;1.4 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026ndash;2.8 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Summary and Conclusion","content":"\u003cp\u003eThis paper presents a comprehensive statistical and wavelet analysis of coronal mass ejection (CME) properties using the manually curated SOHO/LASCO catalog (1996\u0026ndash;2024) and the automated SEEDS catalog (1996\u0026ndash;2022). We grouped CMEs into joint angular width\u0026ndash;speed (cluster I) and width\u0026ndash;acceleration (cluster II). Then, we further stratified the CMEs using their CPAs into low- and high-latitude events in the northern/southern hemispheres. We analyzed annual and relative occurrence rates, fractional contributions of width and speed classes, waiting-time distributions (inter-event intervals) for different classes, and hemispheric asymmetry indices correlated with sunspot numbers. Subsequently, we investigated the periodicities of the CMEs using continuous wavelet transform and global wavelet spectra to identify Rieger-type oscillations (\u0026asymp;\u0026thinsp;3\u0026ndash;11 months) and quasi-biennial oscillations (QBOs; \u0026asymp;1\u0026ndash;3 years) in occurrence rate time series across all latitude bands, hemispheres, and clusters during solar cycles 23 and 24. This multi-parameter framework enabled detailed examination of catalog-dependent sensitivities, solar-cycle modulations, hemispheric and latitudinal asymmetries, waiting-time variations, and the persistence or weakening of mid-term periodicities. Finally, we outlined the summarized key results from this present study below:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNarrow CMEs accounted for a higher share in SEEDS (\u0026asymp;\u0026thinsp;72%) than in LASCO (45%), while regular CMEs made up a smaller portion in SEEDS (27%) compared to LASCO (47%). In terms of overall CME frequency, the LASCO CMEs recorded a higher count during the maximum of SC24 than SC23. However, SEEDS CMEs show the same trend of higher frequencies in both SC23 and SC24. These substantially higher frequencies in SC24 are likely due to weaker poloidal magnetic fields allowing more weak CMEs to escape.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNarrow CME relative frequencies are higher during declining/minimum phases of SC23\u0026ndash;24, while regular CMEs dominate ascending/maxima. Halo and partial CMEs, though relatively less common, show significant correlation with solar activity.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSC23 features elevated relative percentages of solar wind and intermediate-speed CMEs, while SC24 activity shows reduced contributions from fast, solar wind, and slow CMEs, consistent with weaker activity. In contrast, SEEDS consistently ranks slow CMEs as most frequent, followed by intermediate, solar wind, and fast CME types across SC23 and 24.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMost CMEs originate at low latitudes, with high-latitude contributions peaking at SC maxima, but low-latitude CMEs show asynchronous variations to SC phases. hemispheric asymmetries reverse between cycles: LASCO low-latitude CMEs show southern dominance in SC23 (SH\u0026thinsp;\u0026asymp;\u0026thinsp;52%) and northern in SC24 (NH\u0026thinsp;\u0026asymp;\u0026thinsp;55\u0026ndash;61%), while SEEDS exhibits northern in SC23 (NH\u0026thinsp;\u0026asymp;\u0026thinsp;57%), but converges to northern in SC24 (NH\u0026thinsp;\u0026asymp;\u0026thinsp;53\u0026ndash;61%); high-latitude CMEs display strong northern dominance in SC24 (NH\u0026thinsp;\u0026asymp;\u0026thinsp;65\u0026ndash;75%) with larger short-term variability and weaker to moderate SSN correlations (ρ\u0026thinsp;\u0026asymp;\u0026thinsp;0.2\u0026ndash;0.45), potentially driven by weaker polar fields, altered meridional flows, and enhanced rush-to-pole effects linking polar/equatorial regions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWaiting times between consecutive CMEs reveal pronounced asymmetries tied to angular width and SC phases. Narrow CMEs occur less frequently (with longer waiting times) during solar maxima and more often (shorter waiting times) during solar minima, while regular CMEs show the opposite trend. CMEs in SC24 exhibit shorter waiting times than their SC23 counterparts, likely due to weaker post-reversal poloidal fields driving more frequent eruptions. Speed-based patterns add complexity: slow CMEs show reduced waiting times during the rising phase and increased waiting times during the maximum, while solar wind CMEs display the reverse behavior. In general, LASCO records larger waiting time variations than SEEDS.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFor certain CME classes, both the LASCO and SEEDS show a constant QBO short and mid-term periodicity of \u0026asymp;\u0026thinsp;1.3\u0026ndash;2.73\u0026nbsp;year. This includes low- and high-latitude CMEs in the NH, as well as low-latitude CMEs in the SH during SC24, accelerating and decelerating partial and halo CMEs (both global and local), and different speed-width combinations, such as fast and solar wind regular CMEs.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRieger-type periodicities (\u0026asymp;\u0026thinsp;3.7\u0026ndash;6.4 months) are longer in SEEDS than LASCO, stronger at low latitudes (e.g., \u0026asymp;\u0026thinsp;5\u0026ndash;6 months for NH low-latitude in SC23\u0026ndash;24), and weaken at high latitudes, characterized by anomalously longer period in SEEDS SC24 high-latitude southern events. In principle, we find that Rieger-type periods decrease while QBOs increase with CME angular width (narrow to halo).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFinally, we find significant differences in Rieger and QBO periods in hemispheres, latitudes, and solar cycles, with no identical Rieger periods between LASCO and SEEDS. Low-latitude CMEs display greater periodicity diversity, driven by AR magnetic dynamics, while high-latitude CMEs show shorter Rieger-type periodicities. QBO variations\u0026thinsp;\u0026asymp;\u0026thinsp;1.3\u0026nbsp;year are common in local wavelets across both catalogs. CMEs in SOHO/LASCO repositories during SC23 exhibit broader periodic ranges than their counterparts in SEEDS. However, QBO periodicities remain consistent across latitude bands in both cycles.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eImplications and caveats for catalog selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is worth emphasizing that SOHO/LASCO and SEEDS catalogs differ in detection methodology and event characterization, and therefore, their suitability depends strongly on the CME parameter under investigation. Nonetheless, based on our comparative analysis of the SOHO/LASCO and SEEDS catalogs across SC23 and 24, we attempt to provide the following recommendations with caveats for researchers selecting a catalog based on the scientific objectives under consideration:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(i)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;For Studies of CME Occurrence Rate and SC Phase Association:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSEEDS data is more suitable. Our findings show that SEEDS yearly counts, particularly for narrow and regular CMEs, track the sunspot number profile more precisely, especially during solar minimum. However, users should be aware of its cadence correction factor (≈0.63). SOHO/LASCO, on the other hand, may over-represent events during the maximum of weaker cycles (e.g., SC24) due to human identification of expansive and faint structures, which is useful for studying cycle-scale heliospheric pressure effects but may complicate straightforward occurrence rate studies.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(ii)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Angular Width-Dependent Studies:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor studying narrow CMEs: SEEDS is superior, detecting ≈72% of events as narrow compared to ≈45% in LASCO. Its algorithm is more sensitive to faint, small-scale eruptions, making it ideal for investigating mini-CMEs or eruptions during solar minima.\u003c/p\u003e\n\u003cp\u003eTo study regular and partial/halo CMEs: SOHO/LASCO is more reliable. Human experts in the CDAW team are better at identifying the full spatial extent of wider, more complex CMEs and classifying halo events since these types are, in most cases, brighter and visible. SEEDS tends to fragment wider events or miss halos due to projection and background subtraction challenges.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(iii)\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Speed and Kinematic Studies:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor slow- and intermediate-speed CMEs: SEEDS recorded higher frequencies, suggesting its automated edge-tracking in running-difference images is effective for this class of events. However, to study solar wind-speed and fast CME types, as well as acceleration studies, the SOHO/LASCO catalog is recommended. The manual catalog maintains more consistent kinematics for events where precise leading-edge tracking is difficult for automation. Our analysis of periodicities in width-acceleration clusters (cluster II) was more definitive using LASCO data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(iv)\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Latitudinal and hemispheric Asymmetry Studies:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSOHO/LASCO is preferred for investigating hemispheric asymmetries in source location. Its manual CPA determination, while subject to projection effects, provides a more consistent long-term record for latitudinal binning. The asynchronous behavior of low-latitude CMEs in LASCO reveals physically significant asymmetries tied to AR evolution. Researchers should note that SEEDS showed an anomalously long Rieger period for high-latitude SH CMEs in SC24, which may be a real physical signal or an artifact of its detection sensitivity at high latitudes during a weak cycle. Therefore, this requires cross-verification with the SOHO/LASCO catalog.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(v)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Periodicity and Wavelet Analysis:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor Rieger-type periods (≈150 days): SEEDS tends to yield longer period estimates, likely due to its sensitivity to the repeated eruption of faint and narrow CMEs, which may have different drivers. LASCO periods are shorter and may be more representative of the dominant periodic forcing in ARs.\u003c/p\u003e\n\u003cp\u003eFor QBO periodicities: Both catalogs show remarkable consistency in QBO periods (e.g., with more ≈2.73-year frequent signals), especially for regular and partial CMEs. This suggests that for mid-term periodicity studies, either catalog is valid, and the QBO signal is a physical feature largely independent of detection methodology.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(vi)\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;For Waiting Time Analysis:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSOHO/LASCO reveals larger amplitude variations and more pronounced solar-cycle modulation in waiting times, making it better for studying the interplay between CME eruption rhythms and global magnetic field evolution. In contrast, SEEDS waiting times are shorter and show less variation, useful for establishing a baseline \"noise\" floor or studying high-cadence eruption sequences.\u003c/p\u003e\n\u003cp\u003eFinally, it should be noted that no single catalog is generally consistent; however, utilizing both catalogs simultaneously could provide an effective approach for thorough investigations because their differences alone might even reveal some underlying physical processes.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe author declares no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e \u003cp\u003eMany thanks to all contributors of SOHO/LASCO, SEEDS, and WDC-SILSO, Royal Observatory of Belgium, Brussels, for making their catalog publicly available. The SOHO/LASCO catalog is part of the CDAW data center initiative by NASA and The Catholic University of America in cooperation with the Naval Research Laboratory. The SEEDS CME catalog is generated and maintained by the Space Weather Laboratory at George Mason University. Finally, the author extends his outmost gratitude to the reviewer (s) for his/her valuable comments in improving this manuscript\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe SOHO/LASCO data can be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cdaw.gsfc.nasa.gov/CME_list/\u003c/span\u003e\u003cspan address=\"https://cdaw.gsfc.nasa.gov/CME_list/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, SEEDS data available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://spaceweather.gmu.edu/seeds/\u003c/span\u003e\u003cspan address=\"http://spaceweather.gmu.edu/seeds/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, and the Sunspot number can be retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sidc.be/SILSO/datafiles\u003c/span\u003e\u003cspan address=\"https://www.sidc.be/SILSO/datafiles\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarlyaeva T, Wojak J, Lamy P, Boclet B, Toth I (2018) Periodic behaviour of coronal mass ejections, eruptive events, and solar activity proxies during solar cycles 23 and 24. 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ApJ 849:79. https://doi.org/10.3847/1538-4357/aa8e49\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Southwest Research Institute","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Coronal Mass Ejections, solar cycle, Wavelet Analysis, Waiting Times, Periodicities, Local and Global Wavelets","lastPublishedDoi":"10.21203/rs.3.rs-8603202/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8603202/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, we investigate the periodic behavior of coronal mass ejections (CMEs) using statistical and wavelet analysis on SOHO/LASCO (1996\u0026ndash;2024) and SEEDS (1996\u0026ndash;2022) catalogs. We classified CMEs into joint angular width\u0026ndash;speed (cluster I) and width\u0026ndash;acceleration (cluster II), further separated into low- and high-latitude, northern and southern hemisphere subsets, and analyzed their relative occurrence rate, waiting times, and search for Rieger-type and quasi-biennial oscillations (QBOs) across solar cycles (SC) 23\u0026ndash;24. We found that narrow CMEs occur during declining and minimum phases, while regular and partial/halo CMEs show consistency with SC activity. SEEDS shows a larger fraction of narrow CMEs (\u0026asymp;\u0026thinsp;72%) than LASCO (\u0026asymp;\u0026thinsp;45%), indicating catalog-dependent sensitivity to faint and small-scale eruptions. Furthermore, waiting times for narrow and regular CMEs are relatively longer (shorter) during the maximum (minimum) of SC23 (24), due to weaker polar fields and enhanced CME escape in the weaker cycle. Also, low-latitude CMEs dominate over high-latitude CMEs in both hemispheres, but the hemispheric asymmetry reverses between cycles. For instance, LASCO low-latitude CMEs show southern dominance in cycle 23 and northern dominance in cycle 24, whereas SEEDS exhibits opposite low-latitude dominance in cycle 23 but converges to northern dominance in cycle 24. Again, high-latitude CMEs display high northern dominance in SC24 with larger short-term variability and weaker correlation with sunspot number in both catalogs. Moreover, Rieger-type periods (\u0026asymp;\u0026thinsp;3.7\u0026ndash;6.4 months) and QBO signals at 1.3\u0026ndash;2.73\u0026nbsp;year tend to repeat across specific latitudes and CME clusters showing identical\u0026thinsp;\u0026asymp;\u0026thinsp;2.73\u0026nbsp;year periods in both catalogs, while Rieger-type periods weaken toward higher latitudes and are generally longer in SEEDS than in LASCO, with an anomalously long high-latitude southern Rieger period in cycle 24. Finally, Rieger-type periodicities decrease with increasing CME size in LASCO-decelerating CMEs, while QBO periodicities increase with CME size.\u003c/p\u003e","manuscriptTitle":"Unravelling Rieger and Quasi-Biennial Periodic Trends and Asymmetries in Coronal Mass Ejections Using Wavelet Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-18 05:03:04","doi":"10.21203/rs.3.rs-8603202/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53509d76-d83a-4091-b5ca-99cecc7d22e5","owner":[],"postedDate":"January 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61149420,"name":"Astrophysics and Cosmology"}],"tags":[],"updatedAt":"2026-01-18T05:03:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-18 05:03:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8603202","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8603202","identity":"rs-8603202","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}