{"paper":{"title":"Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","hep-ex","hep-ph","physics.ins-det"],"primary_cat":"physics.data-an","authors_text":"A. Barresi, A. Bergnoli, A. Brigatti, A. Budano, A. Cammi, A.C. Re, A. Fabbri, A. Garfagnini, A. Gavrikov, A. Martini, A. Paoloni, A. Romani, A. Serafini, B. Caccianiga, B. Ricci, C. Clementi, C. Coletta, C. Landini, C. Sirignano, C. Tuv\\`e, C. Venettacci, D. Basilico, D. Chiesa, D. Dolzhikov, D. Orestano, E. Percalli, E. Previtali, E. Stanescu Farilla, F. Houria, F. Mantovani, F. Ortica, F. Petrucci, G. Andronico, G. Felici, G. Ferrante, G. Ranucci, G. Verde, I. Lippi, L. Lastrucci, L. Loi, L. Miramonti, L. Pelicci, L. Stanco, L. V. D'Auria, L. Votano, M. Beretta, M. Borghesi, M.D.C Torri, M.G. Giammarchi, M. Gonchar, M. Grassi, M. Montuschi, M. Nastasi, M. Sisti, N. Giudice, N. Guardone, P. Lombardi, R. Brugnera, R. Bruno, R. Caruso, R. M. Guizzetti, S. Dusini, S.M. Mari, V. Antonelli, V. Cerrone, V. Strati","submitted_at":"2025-07-31T07:33:05Z","abstract_excerpt":"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, giv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.23297","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.23297/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}