Inference of neutrino flavor evolution through data assimilation and neural differential equations

TL;DR

This paper introduces a novel inference approach combining data assimilation, neural networks, and differential equations to model neutrino flavor evolution in dense astrophysical environments, improving accuracy and scalability.

Contribution

It develops an inference-based framework integrating neural differential equations and optimization algorithms for neutrino flavor evolution modeling.

Findings

Accurately captures flavor histories in simulated experiments.

Demonstrates effectiveness of evolutionary algorithms in inference.

Provides a scalable approach for complex neutrino systems.

Abstract

The evolution of neutrino flavor in dense environments such as core-collapse supernovae and binary compact object mergers constitutes an important and unsolved problem. Its solution has potential implications for the dynamics and heavy-element nucleosynthesis in these environments. In this paper, we build upon recent work to explore inference-based techniques for estimation of model parameters and neutrino flavor evolution histories. We combine data assimilation, ordinary differential equation solvers, and neural networks to craft an inference approach tailored for non-linear dynamical systems. Using this architecture, and a simple two-neutrino, two-flavor model, we test various optimization algorithms with the help of four experimental setups. We find that employing this new architecture, together with evolutionary optimization algorithms, accurately captures flavor histories in the four experiments. This work provides more options for extending inference techniques to large numbers of neutrinos.