Detecting Fast Neutrino Flavor Conversions with Machine Learning

TL;DR

This paper demonstrates how machine learning can effectively detect fast neutrino flavor conversions in realistic supernova simulation data, improving robustness and extending detection capabilities to different neutrino channels.

Contribution

The study advances ML detection of neutrino flavor crossings by using realistic simulation data and extending methods to heavy-leptonic channels, showing improved robustness and versatility.

Findings

ML effectively detects FFCs in realistic CCSN data.

Simpler ML models outperform complex ones on artificial data.

ML techniques can identify crossings in heavy-leptonic channels.

Abstract

Neutrinos in dense environments like core-collapse supernovae (CCSNe) and neutron star mergers (NSMs) can undergo fast flavor conversions (FFCs) once the angular distribution of neutrino lepton number crosses zero along a certain direction. Recent advancements have demonstrated the effectiveness of machine learning (ML) in detecting these crossings. In this study, we enhance prior research in two significant ways. Firstly, we utilize realistic data from CCSN simulations, where neutrino transport is solved using the full Boltzmann equation. We evaluate the ML methods' adaptability in a real-world context, enhancing their robustness. In particular, we demonstrate that when working with artificial data, simpler models outperform their more complex counterparts, a noteworthy illustration of the bias-variance tradeoff in the context of ML. We also explore methods to improve artificial datasets for ML training. In addition, we extend our ML techniques to detect the crossings in the heavy-leptonic channels, accommodating scenarios where $\nu_x$ and $\bar\nu_x$ may differ. Our research highlights the extensive versatility and effectiveness of ML techniques, presenting an unparalleled opportunity to evaluate the occurrence of FFCs in CCSN and NSM simulations.