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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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.
The paper evaluates how triangular versus two-L-shaped geometries, arm lengths, and presence of low-frequency instruments affect the science reach of the Einstein Telescope for compact binaries, multi-messenger events, and stochastic backgrounds.
citing papers explorer
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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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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.
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Science with the Einstein Telescope: a comparison of different designs
The paper evaluates how triangular versus two-L-shaped geometries, arm lengths, and presence of low-frequency instruments affect the science reach of the Einstein Telescope for compact binaries, multi-messenger events, and stochastic backgrounds.