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

TL;DR

This paper compares machine learning and analytical models for predicting fast neutrino flavor transformations in neutron star mergers, highlighting their accuracy, generalization, and computational trade-offs.

Contribution

It introduces a new fully three-dimensional analytical model and evaluates a machine learning approach for local neutrino flavor transformation predictions.

Findings

ML model performs well but has limited generalization.

Multidimensional analytical model outperforms simpler models.

Analytic models struggle with strong anisotropies.

Abstract

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. Our ML model shows good overall performance, but struggles to generalize to conditions from a NSM simulation not used for training. The multidimensional analytic model performs and generalizes even better, while other analytic models (which assume axisymmetric neutrino distributions) do not have reliably high performances, as they notably fail as expected to account for effects resulting from strong anisotropies. The ML and analytic subgrid models extensively tested here are both promising, with different computational requirements and sources of systematic errors.