Bayesian Inference of Phenomenological EoS of Neutron Stars with Recent Observations

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 ${\rho }_0=2.8\times 10^{14}\mathrm{g}\mathrm{c}{\mathrm{m}}^{-3}$, 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 $2.5M_{\odot }$. 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.

Comments: Minor typo fixes in the title and few typos corrected in the text. Added funding from Brookhaven