Simulation-based inference for precision neutrino physics through neural Monte Carlo tuning

Precise modeling of detector energy response is crucial for next-generation neutrino experiments, which present computational challenges due to the 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 ows 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 ows model enables unbinned likelihood analysis, while the transformer provides an ef cient binned alternative. By providing both options, our framework offers exibility to choose the most appropriate method for speci c needs. Finally, our approach establishes a template for similar applications across experimental neutrino and broader particle physics.