Bayesian inference of neutron-star observables based on effective nuclear interactions

TL;DR

This paper uses Bayesian methods with various nuclear models to constrain the nuclear matter equation of state based on neutron star observations, highlighting model dependence and providing robust symmetry energy estimates.

Contribution

It introduces a Bayesian framework to compare different nuclear models and derive consistent constraints on the neutron star EOS from astrophysical data.

Findings

Symmetry energy at twice saturation density is constrained to about 48 MeV with uncertainties.

Model dependence affects the EOS constraints, especially from relativistic vs. non-relativistic models.

Neutron star mass and radius data favor a stiff isoscalar EOS and moderately soft symmetry energy.

Abstract

Based on the Skyrme-Hartree-Fock model (SHF) as well as its extension (the Korea-IBS-Daegu-SKKU (KIDS) model) and the relativistic mean-field (RMF) model, we have studied the constraints on the parameters of the nuclear matter equation of state (EOS) from adopted astrophysical observables using a Bayesian approach. While the masses and radii of neutron stars generally favors a stiff isoscalar EOS and a moderately soft nuclear symmetry energy, model dependence on the constraints is observed and mostly originates from the incorporation of higher-order EOS parameters and difference between relativistic and non-relativistic models. At twice saturation density, the value of the symmetry energy is constrained to be $48^{+15}_{-11}$ MeV in the standard SHF model, $48^{+8}_{-15}$ MeV in the KIDS model, and $48^{+5}_{-6}$ MeV in the RMF model, around their maximum {\it a posteriori} values within $68\%$ confidence intervals. Our study helps to obtain a robust constraint on nuclear matter EOS, and meanwhile, to understand the model dependence of the results.