Many-Body Simulations of the Fast Flavor Instability

TL;DR

This study uses a novel tensor network approach to show that many-body quantum correlations can disrupt fast neutrino flavor instabilities in supernovae and neutron star mergers, affecting astrophysical phenomena.

Contribution

First demonstration that many-body correlations disrupt fast flavor instability using a tensor network framework with tunable mean-field to many-body transition.

Findings

Many-body correlations can prevent the saturation of flavor instabilities.

The timescale of flavor transformation scales logarithmically with system size.

Results impact understanding of supernova dynamics and neutrino signals.

Abstract

The neutrino fast flavor instability dominates the evolution of neutrino flavor within the engines of core-collapse supernovae and neutron star mergers. However, theoretical models of neutrino flavor change that include many-body quantum correlations can differ starkly from similar mean-field calculations. We demonstrate for the first time that the inhomogeneous fast flavor instability is disrupted by many-body correlations using a novel tensor network framework that allows a continuous transition between mean-field and many-body results by tuning the singular value decomposition cutoff value. Generalizing the forward-scattering Hamiltonian to spatially varying conditions, we demonstrate that the timescale of flavor transformation scales logarithmically with system size, suggesting that many-body effects could occur before mean-field instabilities are able to saturate. Our results have significant implications for astrophysical explosion dynamics, nucleosynthesis, and observable neutrino signatures.