Learning from virtual experiments to assist users of Small Angle Neutron Scattering in model selection

Learning from virtual experiments to assist users of Small Angle Neutron Scattering in model selection | 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 Learning from virtual experiments to assist users of Small Angle Neutron Scattering in model selection José Ignacio Robledo, Henrich Frielinghaus, Peter Willendrup, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4117876/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jun, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract In this work we combine the advantages of Small Angle Neutron Scattering (SANS) Monte Carlo simulations with the recent advances in computer vision to generate a tool that can assist SANS users in form factor model selection. We generate adataset of almost 260.000 SANS virtual experiments of the SANS beamline KWS-1 at FRM-II, Germany, intended for Machine Learning purposes. Then, we train a recommendation system based on an ensemble of Convolutional Neural Networks to predict the form factor model from the two-dimensional SANS scattering pattern measured at the position-sensitive detector of the beamline. The results show that the CNNs can learn the form factor prediction task, and that this recommendation system has a high accuracy in the classification task on 46 different form factor models. We also test the network with real data and explore the outcome. Finally, we discuss the reach of counting with the set of virtual experimental data presented here, and of such a recommendation system in the SANS user data analysis procedure. Physical sciences/Materials science Physical sciences/Physics/Techniques and instrumentation Machine Learning Neutron Convolutional Neural Network SANS dataset Monte Carlo Full Text Additional Declarations No competing interests reported. Supplementary Files KWS1sphere.instr.txt Cite Share Download PDF Status: Published Journal Publication published 30 Jun, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 May, 2024 Reviews received at journal 01 May, 2024 Reviews received at journal 14 Apr, 2024 Reviewers agreed at journal 08 Apr, 2024 Reviewers agreed at journal 05 Apr, 2024 Reviewers invited by journal 05 Apr, 2024 Editor assigned by journal 05 Apr, 2024 Editor invited by journal 02 Apr, 2024 Submission checks completed at journal 01 Apr, 2024 First submitted to journal 17 Mar, 2024 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. 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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-4117876","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":286649957,"identity":"6d0fbee9-eccb-4d00-9ef7-10abb9513ab1","order_by":0,"name":"José Ignacio 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