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kiloHertz gravitational waves from binary neutron star remnants: time-domain model and constraints on extreme matter

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arxiv 1908.11418 v2 pith:HNZNITID submitted 2019-08-29 gr-qc astro-ph.HE

kiloHertz gravitational waves from binary neutron star remnants: time-domain model and constraints on extreme matter

classification gr-qc astro-ph.HE
keywords modelstarextremekilohertzneutronsignalsdensitiesequation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The remnant star of a neutron star merger is an anticipated loud source of kiloHertz gravitational waves that conveys unique information on the equation of state of hot matter at extreme densities. Observations of such signals are hampered by the photon shot noise of ground-based interferometers and pose a challenge for gravitational-wave astronomy. We develop an analytical time-domain waveform model for postmerger signals informed by numerical relativity simulations. The model completes effective-one-body waveforms for quasi-circular nonspinning binaries in the kiloHertz regime. We show that a template-based analysis can detect postmerger signals with a minimal signal-to-noise ratios (SNR) of 8, corresponding to GW170817-like events for third-generation interferometers. Using Bayesian model selection and the complete inspiral-merger-postmerger waveform model it is possible to infer whether the merger outcome is a prompt collapse to a black hole or a remnant star. In the latter case, the radius of the maximum mass (most compact) nonrotating neutron star can be determined to kilometer precision. We demonstrate the feasibility of inferring the stiffness of the equation of state at extreme densities using the quasiuniversal relations deduced from numerical-relativity simulations.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Inferring Neutron-Star Properties from Post-merger Gravitational-wave Spectra with Neural Networks

    gr-qc 2026-05 conditional novelty 6.0

    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.

  2. Distinguishing Neutron Star vs. Low-Mass Black Hole Binaries with Late Inspiral & Postmerger Gravitational Waves $-$ Sensitivity to Transmuted Black Holes and Non-Annihilating Dark Matter

    hep-ph 2025-07 unverdicted novelty 6.0

    Future high-frequency-sensitive GW detectors can distinguish binary neutron star from low-mass black hole mergers in late phases, enabling separation of merger rates and constraints on heavy non-annihilating dark matt...