Application of interpretable data-driven methods for the reconstruction of supernova neutrino energy spectra following fast neutrino flavor conversions

Neutrinos can experience fast flavor conversions (FFCs) in highly dense astrophysical environments, such as core-collapse supernovae and neutron star mergers, potentially affecting energy transport and other processes. The simulation of fast flavor conversions under realistic astrophysical conditions requires substantial computational resources and involves significant analytical challenges. While machine learning methods like Multilayer Perceptrons have been used to accurately predict the asymp