Bidirectional Autoregressive Tracking Method for Solar Filaments

Bidirectional Autoregressive Tracking Method for Solar Filaments | 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 Bidirectional Autoregressive Tracking Method for Solar Filaments Ying Li, Yunfei Yang, Xiaoli Zhang, Song Feng, Wei Dai, Bo Liang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9320067/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The eruption of solar filaments is one of the main causes of hazardous space weather, and their complex evolution is often the key factor leading to the eruption. Therefore, achieving precise tracking of the complete lifecycle of solar filaments is significant for enhancing the capabilities of space weather monitoring and forecasting. In this study, a total of 584 Hα full-disk solar images from three observatories (BBSO, GONG, and SMART) were used to build a data set, including a training set and five testing sets. They represent different observation instruments, varying density of filament trajectories, and different time intervals. A new deep learning method, the bidirectional autoregressive tracking method, named BF-TrackFormer, is proposed to detect and track solar filaments. The designed backward autoregressive branch consisting of an ID Embedding Head and a Recheck Network effectively alleviates the problem of incomplete detection of fragmented filaments caused by splitting, as well as the problem of the solar filament lifecycle being divided into multiple discontinuous trajectories due to incomplete and missed detection. The average evaluation metrics IDF1, MOTA, Prec, Reca, and IDSR of the five testing sets are 66.6%, 40.3%, 72.6%, 67.8%, and 22.8%, respectively. With decreasing intervals, the metrics get better and better. The experimental results show that BF-TrackFormer performs well in detection and tracking solar filaments, especially for fragmented filaments. Solar filaments TrackFormer Bidirectional autoregression Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 02 May, 2026 Reviewers agreed at journal 02 May, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 04 Apr, 2026 Submission checks completed at journal 04 Apr, 2026 First submitted to journal 04 Apr, 2026 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-9320067","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625917731,"identity":"923121e9-53b5-4751-a6da-4787c315e275","order_by":0,"name":"Ying Li","email":"","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Li","suffix":""},{"id":625917732,"identity":"471504ca-f62d-4c4d-a12f-1dd14d52a6fc","order_by":1,"name":"Yunfei 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Therefore, achieving precise tracking of the complete lifecycle of solar filaments is significant for enhancing the capabilities of space weather monitoring and forecasting. In this study, a total of 584 Hα full-disk solar images from three observatories (BBSO, GONG, and SMART) were used to build a data set, including a training set and five testing sets. They represent different observation instruments, varying density of filament trajectories, and different time intervals. A new deep learning method, the bidirectional autoregressive tracking method, named BF-TrackFormer, is proposed to detect and track solar filaments. The designed backward autoregressive branch consisting of an ID Embedding Head and a Recheck Network effectively alleviates the problem of incomplete detection of fragmented filaments caused by splitting, as well as the problem of the solar filament lifecycle being divided into multiple discontinuous trajectories due to incomplete and missed detection. The average evaluation metrics IDF1, MOTA, Prec, Reca, and IDSR of the five testing sets are 66.6%, 40.3%, 72.6%, 67.8%, and 22.8%, respectively. With decreasing intervals, the metrics get better and better. 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