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

TL;DR

This paper introduces neural likelihood estimation methods within simulation-based inference to accurately model detector energy responses in neutrino experiments, exemplified by JUNO, enabling precise parameter inference with minimal biases.

Contribution

It develops neural density estimators, including normalizing flows and transformers, for likelihood modeling in neutrino detector calibration, providing flexible, accurate inference tools.

Findings

Normalizing flows enable unbinned likelihood analysis.

Transformer-based regressors offer efficient binned likelihood alternatives.

Framework achieves near-statistical-limit uncertainties with minimal systematic bias.

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.