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Constraining neutron star properties with a new equation of state insensitive approach

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arxiv 2112.10824 v2 pith:F7XPHVVY submitted 2021-12-20 astro-ph.HE gr-qcnucl-th

classification astro-ph.HEgr-qcnucl-th
keywords lambdaneutronodotpropertiesequationinfermassmeasurement
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abstract

Instead of parameterizing the pressure-density relation of a neutron star (NS), one can parameterize its macroscopic properties such as mass ($M$), radius ($R$), and dimensionless tidal deformability ($\Lambda$) to infer the equation of state (EoS) combining electromagnetic and gravitational wave (GW) observations. We present a new method to parameterize $R(M)$ and $\Lambda(M)$ relations, which approximate the candidate EoSs with accuracy better than 5\% for all masses and span a broad region of $M-R-\Lambda$ plane. Using this method we combine the $M-\Lambda$ measurement from GW170817 and GW190425, and simultaneous $M-R$ measurement of PSR J0030+0451 and PSR J0740+6620 to place joint constraints on NS properties. At 90 \% confidence, we infer $R_{1.4}=12.05_{-0.87}^{+0.98}$ km and $\Lambda_{1.4}=372_{-150}^{+220}$ for a $1.4 M_{\odot}$ NS, and $R_{2.08}=12.65_{-1.46}^{+1.36}$ km for a $2.08 M_{\odot}$ NS. Furthermore, we use the inferred values of the maximum mass of a nonrotating NS $M_{\rm max}=2.52_{-0.29}^{+0.33} M_{\odot}$ to investigate the nature of the secondary objects in three potential neutron star-black hole merger (NSBH) system.

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Cited by 2 Pith papers

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

  1. PSR J0952-0607: Probing the Stiffest Equations of State and r-Mode Suppression Mechanisms

    astro-ph.HE 2025-02 conditional novelty 5.0 of 10

    Using the 709.2 Hz spin of PSR J0952-0607 in Bayesian equation-of-state inference shifts its inferred non-rotating mass to 2.10+0.25-0.24 Msun and makes r-mode stability depend on crust rigidity.

  2. Fast and Accurate Prediction of Neutron Star Structure with Deep Neural Networks

    astro-ph.HE 2026-08 conditional novelty 3.0 of 10

    Feedforward and residual neural networks predict neutron star observables from piecewise polytropic EOS parameters with R^2>0.999 and a ~200x speedup over direct TOV integration.

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