Bayesian Inference of Phenomenological EoS of Neutron Stars with Recent Observations

TL;DR

This paper employs Bayesian and MCMC methods to explore neutron star equations of state at high densities, integrating recent observational data to improve understanding of stellar interiors beyond nuclear saturation density.

Contribution

It introduces a Bayesian framework combined with MCMC to analyze high-density neutron star EoS models using recent gravitational wave data, extending knowledge beyond traditional nuclear density regimes.

Findings

Neutron star masses up to 2.5 solar masses modeled.

Bayesian methods effectively incorporate observational data.

High-density EoS variability characterized with statistical rigor.

Abstract

The description of stellar interior remains as a big challenge for the nuclear astrophysics community. The consolidated knowledge is restricted to density regions around the saturation of hadronic matter $\rho _{0} = 2.8\times 10^{14} {\rm\ g\ cm^{-3}}$, regimes where our nuclear models are successfully applied. As one moves towards higher densities and extreme conditions up to five to twenty times $\rho_{0}$, little can be said about the microphysics of such objects. Here, we employ a Markov Chain Monte Carlo (MCMC) strategy to access the variability of polytropic three-pircewised models for neutron star equation of state. With a fixed description of the hadronic matter, we explore a variety of models for the high density regimes leading to stellar masses up to $2.5\ M_{\odot}$. In addition, we also discuss the use of a Bayesian power regression model with heteroscedastic error. The set of EoS from the Laser Interferometer Gravitational-Wave Observatory (LIGO) was used as inputs and treated as data set for testing case.