Type II and Type III Solar Radio Burst Classification Using Transfer Learning

Type II and Type III Solar Radio Burst Classification Using Transfer Learning | 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 Type II and Type III Solar Radio Burst Classification Using Transfer Learning Herman le Roux, Ruhann Steyn, Du Toit Strauss, Mark Daly, Peter Gallagher, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7254648/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Dec, 2025 Read the published version in Solar Physics → Version 1 posted 7 You are reading this latest preprint version Abstract The Sun periodically emits intense bursts of radio emission called solar radio bursts (SRBs). These bursts can disrupt radio communications and be indicative of large solar events that can disrupt technological infrastructure on Earth and in space. The risks posed by these events highlight the need for automated SRB classification, providing the potential to improve event detection and real-time monitoring, thereby enhancing techniques used to research space weather and related events. Using data recorded by the e-Callisto network, a dataset containing images of radio spectra was created. This dataset consists of three classes; Empty spectrograms, spectrograms containing Type II SRBs, and spectrograms containing Type III SRBs. This dataset was used to fine-tune several popular pre-trained deep learning models to classify Type II and Type III SRBs, including; VGGnet-19, MobileNet, ResNet-152, DenseNet-201 and YOLOv8. The results obtained from testing the models on the testing set ranged between an F1-score of 87% and 92%. The best performing model, YOLOv8, demonstrates that utilising pre-trained models for event classification can provide automated approaches to classying SRBs and provide a practical solution to the limited amount of data samples available for Type II SRBs. solar radio bursts convolutional neural networks transfer learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Dec, 2025 Read the published version in Solar Physics → Version 1 posted Editorial decision: Revision requested 14 Aug, 2025 Reviews received at journal 12 Aug, 2025 Reviewers agreed at journal 08 Aug, 2025 Reviewers invited by journal 08 Aug, 2025 Editor assigned by journal 31 Jul, 2025 Submission checks completed at journal 31 Jul, 2025 First submitted to journal 30 Jul, 2025 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. 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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-7254648","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499619402,"identity":"07f8d965-1e7a-47cb-aaf2-cf63ba9bff88","order_by":0,"name":"Herman le 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