Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning
This provides a flexible template for parameter tuning in neutrino and particle physics experiments, though it appears incremental as an application of existing neural methods to this specific domain.
The researchers tackled the challenge of precise detector energy response modeling for neutrino experiments by developing neural likelihood estimation methods within a simulation-based inference framework, achieving uncertainties limited only by statistics with near-zero systematic biases in a JUNO experiment case study.
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.