Detecting Fast Neutrino Flavor Conversions with Machine Learning

Understand

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.