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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
gr-qc 2verdicts
CONDITIONAL 2representative citing papers
Photon counting readout detects weak postmerger gravitational wave signals at a rate of about 1 in 100 for SNR 0.2 and yields a twofold improvement in neutron star radius measurement after 20,000 events.
citing papers explorer
-
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.
-
Photon counting readout for detection and inference of gravitational waves from neutron star merger remnants
Photon counting readout detects weak postmerger gravitational wave signals at a rate of about 1 in 100 for SNR 0.2 and yields a twofold improvement in neutron star radius measurement after 20,000 events.