Using Bayesian Inference to Distinguish Neutrino Flavor Conversion Scenarios via a Prospective Supernova Neutrino Signal

Understand

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 MSW, we explore several more sophisticated flavor conversion scenarios, such as spectral swapping, fast flavor conversions, flavor equipartition caused by non-standard 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.