Neural networks trained on noise-free post-merger spectra outperform linear regression baselines at predicting neutron-star mass, quadrupolar tidal deformability, and mass-radius slope from numerical-relativity catalogs.
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Analytical expressions for ALP-photon conversion in transient compact stars yield an updated bound g_aγ < 5×10^{-12} GeV^{-1} for m_a ≲ 10^{-9} eV from SN 1987A, plus sensitivity forecasts for future Galactic SN and NSM observations.
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Inferring Neutron-Star Properties from Post-merger Gravitational-wave Spectra with Neural Networks
Neural networks trained on noise-free post-merger spectra outperform linear regression baselines at predicting neutron-star mass, quadrupolar tidal deformability, and mass-radius slope from numerical-relativity catalogs.