Application of Neural Networks for the Reconstruction of Supernova Neutrino Energy Spectra Following Fast Neutrino Flavor Conversions

• URL: http://arxiv.org/abs/2401.17424v1
• Date: Tue, 30 Jan 2024 20:27:28 GMT
• Title: Application of Neural Networks for the Reconstruction of Supernova Neutrino Energy Spectra Following Fast Neutrino Flavor Conversions
• Authors: Sajad Abbar, Meng-Ru Wu, and Zewei Xiong
• Abstract summary: Neutrinos can undergo fast flavor conversions (FFCs) within extremely dense astrophysical environments. We employ physics-informed neural networks (PINNs) to predict the outcomes of FFCs within such a multi-energy neutrino gas.
• Score: 0.24578723416255752
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neutrinos can undergo fast flavor conversions (FFCs) within extremely dense astrophysical environments such as core-collapse supernovae (CCSNe) and neutron star mergers (NSMs). In this study, we explore FFCs in a \emph{multi-energy} neutrino gas, revealing that when the FFC growth rate significantly exceeds that of the vacuum Hamiltonian, all neutrinos (regardless of energy) share a common survival probability dictated by the energy-integrated neutrino spectrum. We then employ physics-informed neural networks (PINNs) to predict the asymptotic outcomes of FFCs within such a multi-energy neutrino gas. These predictions are based on the first two moments of neutrino angular distributions for each energy bin, typically available in state-of-the-art CCSN and NSM simulations. Our PINNs achieve errors as low as $\lesssim6\%$ and $\lesssim 18\%$ for predicting the number of neutrinos in the electron channel and the relative absolute error in the neutrino moments, respectively.

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