Bayesian Inference of Fine-Features of Nuclear Equation of State from Future Neutron Star Radius Measurements to 0.1km Accuracy
Bao-An Li, Xavier Grundler, Wen-Jie Xie, Nai-Bo Zhang

TL;DR
This paper uses Bayesian inference with future neutron star radius measurements to precisely constrain nuclear matter properties and the symmetry energy parameters at high densities, revealing detailed correlations and features in the EOS.
Contribution
It introduces a Bayesian framework to infer detailed nuclear matter parameters from high-precision neutron star radius data, highlighting the impact of measurement accuracy on constraining the EOS.
Findings
Smaller radius measurement errors improve parameter inference.
Double-peak features in parameter PDFs emerge at high measurement precision.
High-precision measurements of canonical NSs better constrain the EOS around 2-3 times saturation density.
Abstract
To more precisely constrain the Equation of State (EOS) of supradense neutron-rich nuclear matter, future high-precision X-ray and gravitational wave observatories are proposed to measure the radii of neutron stars (NSs) with an accuracy better than about 0.1 km. However, it remains unclear what particular aspects (other than the stiffness generally spoken of in the literature) of the EOS and to what precision they will be better constrained. In this work, within a Bayesian framework using a meta-model EOS for NSs, we infer the posterior probability distribution functions (PDFs) of incompressibility and skewness of symmetric nuclear matter (SNM) as well as the slope , curvature , and skewness characterizing the density dependence of nuclear symmetry energy , respectively, from mean values of NS radii consistent with…
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Taxonomy
TopicsNuclear Physics and Applications · Geophysics and Gravity Measurements · Statistical and numerical algorithms
