Copertina

Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Gavrikov, A., Serafini, A., Dolzhikov, D., Garfagnini, A., Gonchar, M., Grassi, M., Brugnera, R., Cerrone, V., D'Auria, L. V., Guizzetti, R. M., Lastrucci, L., Andronico, G., Antonelli, V., Barresi, A., Basilico, D., Beretta, M., Bergnoli, A., Borghesi, M., Brigatti, A., Bruno, R., Budano, A., Caccianiga, B., Cammi, A., Caruso, R., Chiesa, D., Clementi, C., Coletta, C., Dusini, S., Fabbri, A., Felici, G., Ferrante, G., Giammarchi, M. G., Giudice, N., Guardone, N., Houria, F., Landini, C., Lippi, I., Loi, L., Lombardi, P., Mantovani, F., Mari, S. M., Martini, A., Miramonti, L., Montuschi, M., Nastasi, M., Orestano, D., Ortica, F., Paoloni, A., Pelicci, L., Percalli, E., Petrucci, F., Previtali, E., Ranucci, G., Re, A. C., Ricci, B., Romani, A., Sirignano, C., Sisti, M., Stanco, L., Farilla, E. Stanescu, Strati, V., Torri, M. D. C, Tuvè, C., Venettacci, C., Verde, G., Votano, L.
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Data Analysis, Statistics and Probability Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology Instrumentation and Detectors
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

_version_	1866915418708377600
author	Gavrikov, A. Serafini, A. Dolzhikov, D. Garfagnini, A. Gonchar, M. Grassi, M. Brugnera, R. Cerrone, V. D'Auria, L. V. Guizzetti, R. M. Lastrucci, L. Andronico, G. Antonelli, V. Barresi, A. Basilico, D. Beretta, M. Bergnoli, A. Borghesi, M. Brigatti, A. Bruno, R. Budano, A. Caccianiga, B. Cammi, A. Caruso, R. Chiesa, D. Clementi, C. Coletta, C. Dusini, S. Fabbri, A. Felici, G. Ferrante, G. Giammarchi, M. G. Giudice, N. Guardone, N. Houria, F. Landini, C. Lippi, I. Loi, L. Lombardi, P. Mantovani, F. Mari, S. M. Martini, A. Miramonti, L. Montuschi, M. Nastasi, M. Orestano, D. Ortica, F. Paoloni, A. Pelicci, L. Percalli, E. Petrucci, F. Previtali, E. Ranucci, G. Re, A. C. Ricci, B. Romani, A. Sirignano, C. Sisti, M. Stanco, L. Farilla, E. Stanescu Strati, V. Torri, M. D. C Tuvè, C. Venettacci, C. Verde, G. Votano, L.
author_facet	Gavrikov, A. Serafini, A. Dolzhikov, D. Garfagnini, A. Gonchar, M. Grassi, M. Brugnera, R. Cerrone, V. D'Auria, L. V. Guizzetti, R. M. Lastrucci, L. Andronico, G. Antonelli, V. Barresi, A. Basilico, D. Beretta, M. Bergnoli, A. Borghesi, M. Brigatti, A. Bruno, R. Budano, A. Caccianiga, B. Cammi, A. Caruso, R. Chiesa, D. Clementi, C. Coletta, C. Dusini, S. Fabbri, A. Felici, G. Ferrante, G. Giammarchi, M. G. Giudice, N. Guardone, N. Houria, F. Landini, C. Lippi, I. Loi, L. Lombardi, P. Mantovani, F. Mari, S. M. Martini, A. Miramonti, L. Montuschi, M. Nastasi, M. Orestano, D. Ortica, F. Paoloni, A. Pelicci, L. Percalli, E. Petrucci, F. Previtali, E. Ranucci, G. Re, A. C. Ricci, B. Romani, A. Sirignano, C. Sisti, M. Stanco, L. Farilla, E. Stanescu Strati, V. Torri, M. D. C Tuvè, C. Venettacci, C. Verde, G. Votano, L.
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2507_23297
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning Gavrikov, A. Serafini, A. Dolzhikov, D. Garfagnini, A. Gonchar, M. Grassi, M. Brugnera, R. Cerrone, V. D'Auria, L. V. Guizzetti, R. M. Lastrucci, L. Andronico, G. Antonelli, V. Barresi, A. Basilico, D. Beretta, M. Bergnoli, A. Borghesi, M. Brigatti, A. Bruno, R. Budano, A. Caccianiga, B. Cammi, A. Caruso, R. Chiesa, D. Clementi, C. Coletta, C. Dusini, S. Fabbri, A. Felici, G. Ferrante, G. Giammarchi, M. G. Giudice, N. Guardone, N. Houria, F. Landini, C. Lippi, I. Loi, L. Lombardi, P. Mantovani, F. Mari, S. M. Martini, A. Miramonti, L. Montuschi, M. Nastasi, M. Orestano, D. Ortica, F. Paoloni, A. Pelicci, L. Percalli, E. Petrucci, F. Previtali, E. Ranucci, G. Re, A. C. Ricci, B. Romani, A. Sirignano, C. Sisti, M. Stanco, L. Farilla, E. Stanescu Strati, V. Torri, M. D. C Tuvè, C. Venettacci, C. Verde, G. Votano, L. Data Analysis, Statistics and Probability Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology Instrumentation and Detectors 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.
title	Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning
topic	Data Analysis, Statistics and Probability Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology Instrumentation and Detectors
url	https://arxiv.org/abs/2507.23297