Asymptotic-state prediction for fast flavor transformation in neutron star mergers

Understand

Neutrino flavor instabilities appear to be omnipresent in dense astrophysical environments, thus presenting a challenge to large-scale simulations of core-collapse supernovae and neutron star mergers (NSMs).

• Subgrid models offer a path forward, but require an accurate determination of the local outcome of such conversion phenomena.
• Focusing on "fast" instabilities, related to the existence of a crossing between neutrino and antineutrino angular distributions, we consider a range of analytical mixing schemes, including a new, fully three-dimensional one, and also introduce a new machine learning (ML) model.
• We compare the accuracy of these models with the results of several thousands of local dynamical calculations of neutrino evolution from the conditions extracted from classical NSM simulations.