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

Understand

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.