A Hybrid CNN–Transformer Network to EnhanceSolar Magnetogram Resolution for Flare PredictiveAnalytics

A Hybrid CNN–Transformer Network to EnhanceSolar Magnetogram Resolution for Flare PredictiveAnalytics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Hybrid CNN–Transformer Network to EnhanceSolar Magnetogram Resolution for Flare PredictiveAnalytics Vishakha Singh, Divya Punia, V.S. Pandey, Ajay K Sharma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9159480/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 The disparity in spatial resolution between the SOHO/MDI and SDO/HMI magnetogramscreates inconsistencies that hinder reliable long term studies of solar magnetic fields and reduce flareforecasting. To solve this problem, the MagRes-Net a hybrid super-resolution Network that uses convolutionalneural networks (CNNs) to extract features locally and transformer-based self attention toextract global gradients. The designed network reconstructs high-resolution magnetograms from lowerresolution images while maintaining physical structures realistic features of the magnetic field. The modelis trained using MDI-HMI co-aligned image pairs and implemented physics-aware constraints into themodel, such as gradient consistency, and magnetic flux conservation. Through quantitative evaluations,MagRes-Net was better than interpolation and CNN-based methods in terms of peak signal-to-noise ratio (PSNR), mean squared error (RMSE), Learned Perceptual Image Patch Similarity (LPIPS) andstructural similarity index measure (SSIM). Quantitatively, AR coverage increases from 12.23% in nativeMDI observations to 13.25% in the super-resolved output, approaching the SDO/HMI reference valueof 14.48%. it indicates the model successfully recovers resolution-dependent attributes. Additionally,spectral, noise robustness and perceptual analyses demonstrated that MagRes-Net preserves structureat all frequencies, and thus can improve resolution dependent magnetic diagnostic techniques, including;continuity of polarity inversion lines, morphology of strong field regions, and magnetic flux distribution.Therefore, these results demonstrate that the proposed method bridges the cross-instrument resolutiondisparities, allowing for the creation of homogeneous magnetogram data sets that are capable for longterm studies relevant for solar activity and improved flare forecasting. Solar Flares Forecasting Magnetic Fields Photosphere Active Regions Magnetic Fields Image Processing Data Analysis Deep learning Magnetograms Super Resolution Resolution Enhancement Full Text Additional Declarations No competing interests reported. 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9159480","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611401032,"identity":"5942e8b6-421e-4d69-89ef-b3b9daac3e4c","order_by":0,"name":"Vishakha Singh","email":"","orcid":"","institution":"National Institute of Technology Delhi","correspondingAuthor":false,"prefix":"","firstName":"Vishakha","middleName":"","lastName":"Singh","suffix":""},{"id":611401033,"identity":"ae85e8f3-9f65-4466-a52e-a9d3161b39e3","order_by":1,"name":"Divya Punia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDCCA0CcwGDDwMbAYMDwsEECLkhISxoDGxtQSyLRWhgYDjMwQLQQ4S6+2z1mDx7UnM/jk2/e+CBxhwUDf/sBxsMFeLRI3jljbpBw7HYxGxtbsUHiGQkGiTMJDIdn4NFicCPHTCKB7XZiGxuPmURiG9AvN4Du5CGo5d85kBbzHyAt8kRpSWw7ALaFAaTFgJAWyTvHyiQS+5KBWtKKQQ7jMTyT2IBXC9/t5m2SP77ZJc5vPrzxw8e2Ojm544cPf8anhUECjQ9UzNiATwOmllEwCkbBKBgFGAAAXmtNSNF0r2AAAAAASUVORK5CYII=","orcid":"","institution":"National Institute of Technology Delhi","correspondingAuthor":true,"prefix":"","firstName":"Divya","middleName":"","lastName":"Punia","suffix":""},{"id":611401034,"identity":"3021498c-51dc-4242-91c3-647e02abab65","order_by":2,"name":"V.S. 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To solve this problem, the MagRes-Net a hybrid super-resolution Network that uses convolutionalneural networks (CNNs) to extract features locally and transformer-based self attention toextract global gradients. The designed network reconstructs high-resolution magnetograms from lowerresolution images while maintaining physical structures realistic features of the magnetic field. The modelis trained using MDI-HMI co-aligned image pairs and implemented physics-aware constraints into themodel, such as gradient consistency, and magnetic flux conservation. Through quantitative evaluations,MagRes-Net was better than interpolation and CNN-based methods in terms of peak signal-to-noise ratio (PSNR), mean squared error (RMSE), Learned Perceptual Image Patch Similarity (LPIPS) andstructural similarity index measure (SSIM). Quantitatively, AR coverage increases from 12.23% in nativeMDI observations to 13.25% in the super-resolved output, approaching the SDO/HMI reference valueof 14.48%. it indicates the model successfully recovers resolution-dependent attributes. 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