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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Differential rotation in hybrid stars with deconfinement phase transition allows quasi-toroidal configurations with quark matter rings and leads to degeneracies in rotational profiles at mass-radius curve intersections.
Numerical post-merger waveforms indicate that planned 3rd-generation GW detector networks can detect rotational instabilities in BNS remnants at distances up to 200 Mpc with a high-frequency design, and the main post-merger peak at 40 Mpc with upgraded HLV.
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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.