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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Future high-frequency-sensitive GW detectors can distinguish binary neutron star from low-mass black hole mergers in late phases, enabling separation of merger rates and constraints on heavy non-annihilating dark matter via transmuted black holes.
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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.
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Distinguishing Neutron Star vs. Low-Mass Black Hole Binaries with Late Inspiral & Postmerger Gravitational Waves $-$ Sensitivity to Transmuted Black Holes and Non-Annihilating Dark Matter
Future high-frequency-sensitive GW detectors can distinguish binary neutron star from low-mass black hole mergers in late phases, enabling separation of merger rates and constraints on heavy non-annihilating dark matter via transmuted black holes.