Constraining the Generalized Tolman-Oppenheimer-Volkoff (GTOV) equation with Bayesian analysis

TL;DR

This paper uses Bayesian inference to constrain parameters of the Generalized TOV equation using neutron star observational data, improving agreement with observations over traditional models.

Contribution

It introduces a Bayesian approach to constrain the GTOV equation parameters with diverse neutron star data, considering different physical scenarios and models.

Findings

Enhanced agreement with observational data

Improved physical quantity estimates

Better constraints on neutron star models

Abstract

In this work, we constrain the values of the parameters of the Generalized Tolman-Oppenheimer-Volkoff (GTOV) equation through Bayesian inference. We use the mass and radius data from the Neutron Star Interior Composition Explorer (NICER) for PSR J0740$+$6620 and PSR J0030$+$0451, as well as the mass, radius, and dimensionless tidal deformability from the gravitational wave (GW) events GW190814 and GW170817. We use two distinct parameterizations of the extended non-linear Walecka model (eNLW) with and without hyperons. The GTOV employed for the study contains additional free parameters with different physical motivations. Two possible scenarios are considered in our analysis: conservative and speculative. In the first case, we take into account the most reliable neutron star (NS) data from NICER and the GW170817 event. In the second case, we consider the possibility that the compact object with a mass of $2.54 M_{\odot}$ in the GW190814 event is an NS. Our findings show significant improvements in the physical quantities analyzed, leading to better agreement with the observational data compared to the results obtained using the TOV equation.