Simulation-based Inference for Precision Neutrino Physics through Neural Monte Carlo Tuning

Simulation-based Inference for Precision Neutrino Physics through Neural Monte Carlo Tuning | 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 Simulation-based Inference for Precision Neutrino Physics through Neural Monte Carlo Tuning Arsenii Gavrikov, Andrea Serafini, Dmitry Dolzhikov, Alberto Garfagnini, and 62 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7291864/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jan, 2026 Read the published version in Communications Physics → Version 1 posted You are reading this latest preprint version Abstract Precise modeling of detector energy response is crucial for next-generation neutrino experiments which present computational challenges due to lack of analytical likelihoods. We propose a solution using neural likelihood estimation within the simulation-based inference framework. We develop two complementary neural density estimators that model likelihoods of calibration data: conditional normalizing flows and a transformer-based regressor. We adopt JUNO --- a large neutrino experiment --- as a case study. The energy response of JUNO depends on several parameters, all of which should be tuned, given their non-linear behavior and strong correlations in the calibration data. To this end, we integrate the modeled likelihoods with Bayesian nested sampling for parameter inference, achieving uncertainties limited only by statistics with near-zero systematic biases. The normalizing flows model enables unbinned likelihood analysis, while the transformer provides an efficient binned alternative. By providing both options, our framework offers flexibility to choose the most appropriate method for specific needs. Finally, our approach establishes a template for similar applications across experimental neutrino and broader particle physics. Physical sciences/Physics/Particle physics/Experimental particle physics Physical sciences/Mathematics and computing/Computational science experimental particle physics large-scale neutrino detectors simulation-based inference neural likelihood estimation conditional density estimation Bayesian inference normalizing flows transformer uncertainty quantification Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 23 Jan, 2026 Read the published version in Communications Physics → 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-7291864","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":495441270,"identity":"3eb2a086-423d-41d7-8c13-1ceb09a470d8","order_by":0,"name":"Arsenii 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conditional density estimation, Bayesian inference, normalizing flows, transformer, uncertainty quantification","lastPublishedDoi":"10.21203/rs.3.rs-7291864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7291864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Precise modeling of detector energy response is crucial for next-generation neutrino experiments which present computational challenges due to lack of analytical likelihoods. We propose a solution using neural likelihood estimation within the simulation-based inference framework. We develop two complementary neural density estimators that model likelihoods of calibration data: conditional normalizing flows and a transformer-based regressor. We adopt JUNO --- a large neutrino experiment --- as a case study. The energy response of JUNO depends on several parameters, all of which should be tuned, given their non-linear behavior and strong correlations in the calibration data. To this end, we integrate the modeled likelihoods with Bayesian nested sampling for parameter inference, achieving uncertainties limited only by statistics with near-zero systematic biases. The normalizing flows model enables unbinned likelihood analysis, while the transformer provides an efficient binned alternative. By providing both options, our framework offers flexibility to choose the most appropriate method for specific needs. Finally, our approach establishes a template for similar applications across experimental neutrino and broader particle physics.","manuscriptTitle":"Simulation-based Inference for Precision Neutrino Physics through Neural Monte Carlo Tuning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 03:03:31","doi":"10.21203/rs.3.rs-7291864/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-physics","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsphys","sideBox":"Learn more about [Communications Physics](http://www.nature.com/commsphys/)","snPcode":"","submissionUrl":"","title":"Communications Physics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a14b29bd-9523-4d7e-98e8-89b7d459948e","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52616041,"name":"Physical sciences/Physics/Particle physics/Experimental particle physics"},{"id":52616042,"name":"Physical sciences/Mathematics and computing/Computational science"}],"tags":[],"updatedAt":"2026-02-17T08:11:26+00:00","versionOfRecord":{"articleIdentity":"rs-7291864","link":"https://doi.org/10.1038/s42005-026-02499-6","journal":{"identity":"communications-physics","isVorOnly":false,"title":"Communications Physics"},"publishedOn":"2026-01-23 05:00:00","publishedOnDateReadable":"January 23rd, 2026"},"versionCreatedAt":"2025-08-12 03:03:31","video":"","vorDoi":"10.1038/s42005-026-02499-6","vorDoiUrl":"https://doi.org/10.1038/s42005-026-02499-6","workflowStages":[]},"version":"v1","identity":"rs-7291864","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7291864","identity":"rs-7291864","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}