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As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Articles
Application of neural networks for the reconstruction of supernova neutrino energy spectra following fast neutrino flavor conversions
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 multienergy 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.
Oscillations of high-energy cosmic neutrinos in the copious MeV neutrino background
The core-collapse of massive stars and the merger of neutron star binaries are among the most promising candidate sites for the production of high-energy cosmic neutrinos. We demonstrate that the high-energy neutrinos produced in such extreme environments can experience efficient flavor conversions on scales much shorter than those expected in vacuum, due to their coherent forward scatterings with the bath of decohered low-energy neutrinos emitted from the central engine.
Evaluating approximate asymptotic distributions for fast neutrino flavor conversions in a periodic 1D box
The fast flavor conversions (FFCs) of neutrinos generally exist in core-collapse supernovae and binary neutron-star merger remnants and can significantly change the flavor composition and affect the dynamics and nucleosynthesis processes.
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