Multiview CNN Architectures for Primary Particle Classification in Extensive Air Showers

Multiview CNN Architectures for Primary Particle Classification in Extensive Air Showers | 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 Multiview CNN Architectures for Primary Particle Classification in Extensive Air Showers Xochitl Silvestre-Gutierrez, Raquel Diaz-Hernandez, Leopoldo Altamirano-Robles, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8197662/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Extensive air showers (EAS) from hadronic and gamma-ray origins are central to high-energy astrophysics, which has motivated the development of automated classification methods. This study explores the use of deep convolutional neural networks (CNNs) for EAS classification using CORSIKA 7.7550 simulations, which generate synthetic data on cosmic-ray cascades. Multiview images across the XZ, YZ, and XY planes, in both color and grayscale, were constructed to provide a robust training dataset. The proposed CNN framework was designed to enhance the separation between hadronic and gamma-induced events, supporting the accurate identification of primary particles, which is a key requirement for rapid characterization of high-energy cosmic phenomena.In this study, neural architectures (ResNet-50, Compact, and AlexNet) were systematically evaluated across different energies and zenith angles, considering particles such as iron, protons, positive muons, and gamma rays. The results indicate a strong performance in classifying heavy nuclei and muons, whereas distinguishing protons from gamma rays remains challenging because of the complex morphology of their cascades and atmospheric interactions. Among the tested models, Compact achieved an accuracy of up to 97.22% on grayscale images with a minimal inference time (1.19 ms), whereas ResNet-50 achieved an accuracy of 97.00 % using color images with data augmentation. These findings highlight the potential of CNN-based approaches for advancing automated EAS analysis in high-energy astrophysics. Extensive Air Showers Convolutional Neural Networks CORSIKA Simulations Gamma-Hadron Classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 13 Jan, 2026 Editor assigned by journal 29 Nov, 2025 Submission checks completed at journal 25 Nov, 2025 First submitted to journal 24 Nov, 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. 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-8197662","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573666731,"identity":"7721a390-1838-451c-b3a8-14717219bf12","order_by":0,"name":"Xochitl 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