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Phenomenological model of gravitational self-force enhanced tides in inspiralling binary neutron stars

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arxiv 2407.08538 v2 pith:BD45QGAY submitted 2024-07-11 gr-qc astro-ph.HE

classification gr-qcastro-ph.HE
keywords neutronmodelbinarytidalphenomgsfstargravitationalstars
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Gravitational waves from inspiralling binary neutron stars provide unique access to ultra-dense nuclear matter and offer the ability to constrain the currently unknown neutron star equation-of-state through tidal measurements. This, however, requires the availability of accurate and efficient tidal waveform models. In this paper we present PhenomGSF, a new phenomenological tidal phase model for the inspiral of neutron stars in the frequency-domain, which captures the gravitational self-force informed tidal contributions of the time-domain effective-one-body model TEOBResumS. PhenomGSF is highly faithful and computationally efficient, and by choosing a modular approach, it can be used in conjunction with any frequency-domain binary black hole waveform model to generate the complete phase for a binary neutron star inspiral. PhenomGSF is valid for neutron star binaries with unequal masses and mass ratios between 1 and 3, and dimensionless tidal deformabilities up to 5000. Furthermore, PhenomGSF does not assume universal relations or parameterised equations-of-state, hence allowing for exotic matter analyses and beyond standard model physics investigations. We demonstrate the efficacy and accuracy of our model through comparisons against TEOBResumS, numerical relativity waveforms and full Bayesian inference, including a reanalysis of the binary neutron star observation GW170817.

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  1. Data-driven approach for extracting tidal information from neutron star binary mergers observed with the Einstein Telescope

    gr-qc 2025-01 conditional novelty 6.0 of 10

    A simulation study showing that the tidal phase of neutron-star mergers can be inferred directly from Einstein Telescope data by fitting six free polynomial coefficients and combining posteriors across events.

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