A Bayesian inference of relativistic mean-field model for neutron star matter from observation of NICER and GW170817/AT2017gfo
Zhenyu Zhu, Ang Li, Tong Liu

TL;DR
This paper uses Bayesian inference combining NICER and GW170817/AT2017gfo observations to constrain the neutron star matter equation of state, revealing bimodal tidal deformability distributions and insights into nuclear matter properties.
Contribution
It introduces a Bayesian framework integrating multi-messenger data to refine the nuclear matter equation of state and related properties in neutron stars.
Findings
Bimodal distribution of tidal deformability posterior.
NICER data stiffens the EOS posterior.
Results on nuclear incompressibility and symmetry energy.
Abstract
The observations of optical and near-infrared counterparts of binary neutron star mergers not only enrich our knowledge about the abundance of heavy elements in the Universe, or help reveal the remnant object just after the merger as generally known, but also can effectively constrain dense nuclear matter properties and the equation of state (EOS) in the interior of the merging stars. Following the relativistic mean-field description of nuclear matter, we perform the Bayesian inference of the EOS and the nuclear matter properties using the first multi-messenger event GW170817/AT2017gfo, together with the NICER mass-radius measurements of pulsars. The kilonova is described by a radiation-transfer model with the dynamical ejecta, and light curves connect with the EOS through the quasi-universal relations between the ejecta properties (the ejected mass, velocity, opacity or electron…
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Taxonomy
TopicsGamma-ray bursts and supernovae · Pulsars and Gravitational Waves Research
