Applications of Machine Learning to Detecting Fast Neutrino Flavor Instabilities in Core-Collapse Supernova and Neutron Star Merger Models

Abstract

Neutrinos propagating in a dense neutrino gas, such as those expected in core-collapse supernovae (CCSNe) and neutron star mergers (NSMs), can experience fast flavor conversions on relatively short scales. This can happen if the neutrino electron lepton number ($ν$ELN) angular distribution crosses zero in a certain direction. Despite this, most of the state-of-the-art CCSN and NSM simulations do not provide such detailed angular information and instead, supply only a few moments of the neutrino angular distributions. In this study we employ, for the \emph{first} time, a machine learning (ML) approach to this problem and show that it can be extremely successful in detecting $ν$ELN crossings on the basis of its zeroth and first moments. We observe that an accuracy of $\sim95\%$ can be achieved by the ML algorithms, which almost corresponds to the Bayes error rate of our problem. Considering its remarkable efficiency and agility, the ML approach provides one with an unprecedented opportunity to evaluate the occurrence of FFCs in CCSN and NSM simulations \emph{on the fly}. We also provide our ML methodologies on GitHub.