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Effects of waveform systematics on inferences of neutron star population properties and the nuclear equation of state

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arxiv 2410.14674 v1 pith:225XN4TN submitted 2024-10-18 gr-qc astro-ph.HE

Effects of waveform systematics on inferences of neutron star population properties and the nuclear equation of state

classification gr-qc astro-ph.HE
keywords inferenceneutroneventsmatternuclearpopulationequation-of-stateextreme
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Gravitational waves from inspiralling neutron stars carry information about matter at extreme gravity and density. The binary neutron star (BNS) event GW170817 provided, for the first time, insight into dense matter through this window. Since then, another BNS (GW190425) and several neutron star-black hole events have been detected, although the tidal measurements were not expected to be well-constrained from them. Collective information regarding the behavior of nuclear matter at extreme densities can be done by performing a joint population inference for the masses, spins, and equation-of-state [1] to enable better understanding. This population inference, in turn, relies on accurate estimates of intrinsic parameters of individual events. In this study, we investigate how the differences in parameter inference of BNS events using different waveform models can affect the eventual inference of the nuclear equation-of-state. We use the state-of-the-art model TEOBResumS with IMRPhenomD NRTidalv2 as a comparison model.

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    Causal convolutional neural networks reconstruct neutron star observables for static, Keplerian, and rotating configurations in about 50 milliseconds per equation of state, compared to 30 minutes with traditional RNS ...