Bayesian Inference of Phenomenological EoS of Neutron Stars with Recent Observations

• Main Authors: Chimanski, Emanuel V, Lobato, Ronaldo V, Goncalves, Andre R, Bertulani, Carlos A
• Format: Journal Article
• Language: English
• Published: 02.05.2022
• Subjects: Physics - High Energy Astrophysical Phenomena; Physics - Nuclear Theory
• DOI: 10.48550/arxiv.2205.01174

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.