Complete leading-order dynamical tidal corrections to neutron-star binaries are derived in EFT, showing dynamical Love numbers enhanced relative to static ones and yielding measurable contributions to the GW phase at 8PN order.
Baiotti, Prog
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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.
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
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Dynamical Tidal Response of Neutron Stars: from Effective Field Theory to Gravitational Waveforms
Complete leading-order dynamical tidal corrections to neutron-star binaries are derived in EFT, showing dynamical Love numbers enhanced relative to static ones and yielding measurable contributions to the GW phase at 8PN order.
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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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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.