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.
Baiotti, Prog
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
gr-qc 2representative citing papers
Hierarchical Bayesian inference on 20 high-SNR simulated binary neutron star events shows a linear lnΛ-lnQ relation suffices and constrains dynamical Chern-Simons gravity length scale to ≤10 km.
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.
-
Inferring neutron-star Love-Q relations from gravitational waves in the hierarchical Bayesian framework
Hierarchical Bayesian inference on 20 high-SNR simulated binary neutron star events shows a linear lnΛ-lnQ relation suffices and constrains dynamical Chern-Simons gravity length scale to ≤10 km.