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The Accuracy of Neutron Star Radius Measurement with the Next Generation of Terrestrial Gravitational-Wave Observatories

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arxiv 2307.05376 v3 pith:IAI3DKD5 submitted 2023-07-10 gr-qc astro-ph.HE

classification gr-qcastro-ph.HE
keywords observatorieswillmassesneutroneventsmeasurementnetworkradius
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In this paper, we explore the prospect for improving the measurement accuracy of masses and radii of neutron stars. We consider imminent and long-term upgrades of the Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo, as well as next-generation observatories -- the Cosmic Explorer and Einstein Telescope. We find that neutron star radius with single events will be constrained to within roughly 500m with the current generation of detectors and their upgrades. This will improve to 200m, 100m and 50m with a network of observatories that contain one, two or three next-generation observatories, respectively. Combining events in bins of 0.05 solar masses we find that for stiffer (softer) equations-of-state like ALF2 (APR4), a network of three XG observatories will determine the radius to within 30m (100m) over the entire mass range of neutron stars from 1 to 2.0 solar masses (2.2 solar masses), allowed by the respective equations-of-state. Neutron star masses will be measured to within 0.5 percent with three XG observatories irrespective of the actual equation-of-state. Measurement accuracies will be a factor of 4 or 2 worse if the network contains only one or two XG observatories, respectively, and a factor of 10 worse in the case of networks consisting of Advanced LIGO, Virgo KAGRA and their upgrades. Tens to hundreds of high-fidelity events detected by future observatories will allow us to accurately measure the mass-radius curve and hence determine the dense matter equation-of-state to exquisite precision.

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Cited by 4 Pith papers

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

    gr-qc 2026-05 conditional novelty 6.0 of 10

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    gr-qc 2025-07 conditional novelty 6.0 of 10

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    A design case study in which a course syllabus is reauthored into a singing-avatar video via an AI pipeline, with a claimed reproducible workflow and public code but no empirical evaluation.

  4. Bayesian analysis of the shear modulus in the neutron-star crust

    astro-ph.HE 2026-06 unverdicted novelty 4.0 of 10

    Bayesian modeling with informed priors reduces uncertainties in neutron-star crust shear properties, predicting torsional mode frequencies of 20-50 Hz compatible with observations.

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