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Application of Neural Networks for the Reconstruction of Supernova Neutrino Energy Spectra Following Fast Neutrino Flavor Conversions
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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.
Forward citations
Cited by 4 Pith papers
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Theory of neutrino slow flavor evolution. Part I. Homogeneous medium
Slow neutrino flavor instabilities split into a new resonant small-scale branch with growth rate ~omega_E/epsilon and the familiar non-resonant branch with the traditional scale 1/sqrt(omega_E mu).
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Comparative Testing of Subgrid Models for Fast Neutrino Flavor Conversions in Core-collapse Supernova Simulations
A 1D supernova simulation with four-species BGK subgrid modeling shows that three-species assumptions overestimate flavor conversion and that semi-implicit time integration is the most reliable.
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Flavor Equilibration of Supernova Neutrinos: Exploring the Dynamics of Slow Modes
Slow flavor conversion drives supernova neutrinos to a coarse-grained flavor-equilibrated state within a few inverse vacuum frequencies, with survival probabilities set by the neutrino-antineutrino density ratio alone.
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Quasi-steady evolution of fast neutrino-flavor conversions
A phenomenological model analytically describes the quasi-steady evolution of fast neutrino-flavor conversions with continuous injection, matching 1D two-beam simulations.
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