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PSR J0030+0451 Mass and Radius from NICER Data and Implications for the Properties of Neutron Star Matter

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TLDR
In this paper, the mass and radius of the isolated 205.53 Hz millisecond pulsar PSR J0030+0451 were estimated using a Bayesian inference approach to analyze its energy-dependent thermal X-ray waveform, which was observed using the Neutron Star Interior Composition Explorer (NICER).
Abstract
Neutron stars are not only of astrophysical interest, but are also of great interest to nuclear physicists because their attributes can be used to determine the properties of the dense matter in their cores. One of the most informative approaches for determining the equation of state (EoS) of this dense matter is to measure both a star’s equatorial circumferential radius R e and its gravitational mass M. Here we report estimates of the mass and radius of the isolated 205.53 Hz millisecond pulsar PSR J0030+0451 obtained using a Bayesian inference approach to analyze its energy-dependent thermal X-ray waveform, which was observed using the Neutron Star Interior Composition Explorer (NICER). This approach is thought to be less subject to systematic errors than other approaches for estimating neutron star radii. We explored a variety of emission patterns on the stellar surface. Our best-fit model has three oval, uniform-temperature emitting spots and provides an excellent description of the pulse waveform observed using NICER. The radius and mass estimates given by this model are km and (68%). The independent analysis reported in the companion paper by Riley et al. explores different emitting spot models, but finds spot shapes and locations and estimates of R e and M that are consistent with those found in this work. We show that our measurements of R e and M for PSR J0030+0451 improve the astrophysical constraints on the EoS of cold, catalyzed matter above nuclear saturation density.

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Citations
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The phenomenology of dynamical neutron star tides

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Distinguishing double neutron star from neutron star-black hole binary populations with gravitational wave observations

TL;DR: In this article, a new data analysis strategy powered by Bayesian inference and machine learning was proposed to identify low-mass black holes, using the distribution of the tidal deformability parameter inferred from gravitational-wave observations.
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Relativistic stars in 4D Einstein-Gauss-Bonnet gravity

TL;DR: In this article, the mass-radius relations of relativistic stars in 4D Einstein-Gauss-Bonnet gravity were obtained for realistic hadronic and for strange quark star equations of state.
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Microscopic equation of state of hot nuclear matter for numerical relativity simulations

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Constraint on phase transition with the multimessenger data of neutron stars

TL;DR: In this paper, the authors perform the Bayesian parameter inference with the gravitational wave data (GW170817) and mass-radius observations of some neutron star (NS) matter using phenomenologically constructed EoS models to search for a potential first-order phase transition.
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B. P. Abbott, +1134 more
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