Approximate Energy-Integration Method for Identifying Collisional Neutrino Flavor Instabilities

TL;DR

This paper introduces a new approximate energy-integration method that efficiently and accurately identifies collisional neutrino flavor instabilities in complex astrophysical environments, improving over previous schemes.

Contribution

The authors develop a robust approximation scheme that preserves key physics and reduces computational cost for detecting neutrino flavor instabilities in supernova and neutron star merger models.

Findings

The new method accurately computes real frequencies and growth rates.

It performs well across isotropic and anisotropic distributions.

It is scalable for high-energy astrophysical simulations.

Abstract

We present an approximate energy-integration method for identifying collisional neutrino flavor instabilities. Direct evaluation of the dispersion relation requires multi-dimensional integrals over neutrino phase space, making systematic searches for unstable modes in numerical models of core-collapse supernovae (CCSNe) and binary neutron star mergers (BNSMs) computationally expensive. In the literature there are some approximate schemes, but they are largely restricted to the homogeneous limit and can exhibit inaccuracies as reported in recent studies. In the current paper, we clarify the origin of the limitations in previous schemes and provide a better approximation method that robustly preserves the key physics of spectral asymmetries and collision rates. It yields a reduced dispersion relation that is inexpensive to evaluate. Comparison with exact solutions demonstrates that our new approximate method shows a good performance in computing both real frequencies and growth rates across a wide range of regimes, including isotropic and anisotropic neutrino distributions for both homogeneous and inhomogeneous modes. This provides a practical, accurate, and scalable framework for identifying collisional flavor instabilities in high-energy astrophysical simulations such as CCSNe and BNSMs.