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

A Gavrikov, A Serafini, D Dolzhikov… - arXiv preprint arXiv …, 2025 - arxiv.org
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… We propose a solution using neural likelihood estimation within the simulation-based inference framework. We develop two complementary neural density estimators …

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@article{2507.23297v1,
Author        = {A. Gavrikov and A. Serafini and D. Dolzhikov and A. Garfagnini and M. Gonchar and M. Grassi and R. Brugnera and V. Cerrone and L. V. D'Auria and R. M. Guizzetti and L. Lastrucci and G. Andronico and V. Antonelli and A. Barresi and D. Basilico and M. Beretta and A. Bergnoli and M. Borghesi and A. Brigatti and R. Bruno and A. Budano and B. Caccianiga and A. Cammi and R. Caruso and D. Chiesa and C. Clementi and C. Coletta and S. Dusini and A. Fabbri and G. Felici and G. Ferrante and M. G. Giammarchi and N. Giudice and N. Guardone and F. Houria and C. Landini and I. Lippi and L. Loi and P. Lombardi and F. Mantovani and S. M. Mari and A. Martini and L. Miramonti and M. Montuschi and M. Nastasi and D. Orestano and F. Ortica and A. Paoloni and L. Pelicci and E. Percalli and F. Petrucci and E. Previtali and G. Ranucci and A. C. Re and B. Ricci and A. Romani and C. Sirignano and M. Sisti and L. Stanco and E. Stanescu Farilla and V. Strati and M. D. C Torri and C. Tuvè and C. Venettacci and G. Verde and L. Votano},
Title         = {Simulation-based inference for Precision Neutrino Physics through Neural
  Monte Carlo tuning},
Eprint        = {2507.23297v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {physics.data-an},
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.},
Year          = {2025},
Month         = {Jul},
Url           = {http://arxiv.org/abs/2507.23297v1},
File          = {2507.23297v1.pdf}
}

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