Higher-Order Spatial Mode Detection Leveraging Deep Learning on Random Optical Patterns

Higher-Order Spatial Mode Detection Leveraging Deep Learning on Random Optical Patterns | 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 Article Higher-Order Spatial Mode Detection Leveraging Deep Learning on Random Optical Patterns Shiva Shankar Mutupuri, MD. Haider Ansari, Satish Anamalamudi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8444410/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 Laguerre-Gauss (LG) beams, characterized by their helical wavefronts and carrying orbital angular momentum (OAM) of ℓℏ where ℓ is the topological charge (TC), hold significant promise for optical communication, imaging,and quantum information science. However, accurately detecting higher topological charges (ℓ ≤ 50) when distorted by scattering media remains a substantialchallenge due to wavefront distortion and speckle formation. This work addressesthis limitation by proposing and evaluating two deep learning architectures, a Convolutional Neural Network (CNN) based model and a Vision Transformer (ViT)based model. Experimental results show that the ViT based model achieves a better mean classification accuracy of 98.1%, outperforming the CNN based modelby approximately 3.3%.Additionally, we validated the robustness of the proposedmodels by accurately detecting the sign of TC along with the magnitude. Unlike existing approaches, our method detects the TC using only a patch of thedistorted intensity profile rather than the full beam. This capability, especiallyfor high TCs in scattering environments, shows the way for more reliable, highcapacity optical systems in real world applications. Physical sciences/Mathematics and computing Physical sciences/Optics and photonics Physical sciences/Physics 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-8444410","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":572227452,"identity":"62a9f0f2-077e-4bb8-a727-91c63773c3da","order_by":0,"name":"Shiva Shankar Mutupuri","email":"","orcid":"","institution":"SRM University, Andhra Pradesh","correspondingAuthor":false,"prefix":"","firstName":"Shiva","middleName":"Shankar","lastName":"Mutupuri","suffix":""},{"id":572227453,"identity":"737d6f2a-03d2-4703-8191-0fc1e824d54a","order_by":1,"name":"MD. 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However, accurately detecting higher topological charges (ℓ ≤ 50) when distorted by scattering media remains a substantialchallenge due to wavefront distortion and speckle formation. This work addressesthis limitation by proposing and evaluating two deep learning architectures, a Convolutional Neural Network (CNN) based model and a Vision Transformer (ViT)based model. Experimental results show that the ViT based model achieves a better mean classification accuracy of 98.1%, outperforming the CNN based modelby approximately 3.3%.Additionally, we validated the robustness of the proposedmodels by accurately detecting the sign of TC along with the magnitude. Unlike existing approaches, our method detects the TC using only a patch of thedistorted intensity profile rather than the full beam. 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