Machine Learning Detection of Non-Axisymmetric Fast Flavor Instabilities in Compact Objects

Abstract

Neutrinos in dense astrophysical environments such as core-collapse supernovae (CCSNe) and neutron star mergers (NSMs) can undergo FFCs, which could develop on extremely small scales. A necessary condition for the occurrence of FFCs is the presence of a zero crossing in the electron lepton number (ELN) angular distribution of neutrinos. In this work, we explore machine learning (ML) approaches to detect non-axisymmetric ELN crossings in these environments, based on input features of the $\nu_e$ and $\bar\nu_e$ zeroth and first angular moments. Overall, the ML models demonstrate relatively good generalizability for most of the unseen test datasets generated by various methods that do not assume the same underlying angular distributions as used in the training set. Interestingly, while the model's performance is mediocre for an axisymmetric distribution dataset derived by solving the discretized Boltzmann transport equation under 1D CCSN background, imposing an artificial non-axisymmetry substantially improves the performance. We also find that for the flavor-equilibrated angular distributions, although our ML model trained based solely on ELN inputs performs poorly when the true crossings depend on the post-equilibrated angular distributions of heavy lepton neutrinos and antineutrinos, which become different, it delivers strong performance in detecting ELN crossings when the heavy-lepton neutrino and antineutrino distributions are artificially removed. This highlights the need for additional input features to further improve the model. This is a crucial step toward successfully integrating FFCs into large-scale CCSN and NSM simulations.