Using Bayesian inference to distinguish neutrino flavor conversion scenarios via a prospective supernova neutrino signal

Abstract

The upcoming galactic core-collapse supernova is expected to produce a considerable number of neutrino events within terrestrial detectors. By using Bayesian inference techniques, we address the feasibility of distinguishing among various neutrino flavor conversion scenarios in the supernova environment, using such a neutrino signal. In addition to the conventional Mikheev-Smirnov-Wolfenstein, we explore several more sophisticated flavor conversion scenarios, such as spectral swapping, fast flavor conversions, flavor equipartition caused by nonstandard neutrino interactions, magnetically induced flavor equilibration, and flavor equilibrium resulting from slow flavor conversions. Our analysis demonstrates that with a sufficiently large number of neutrino events during the supernova accretion phase (exceeding several hundreds), there exists a good probability of distinguishing among feasible neutrino flavor conversion scenarios in the supernova environment. Published by the American Physical Society 2025