Bayesian constraints on covariant density functional equations of state of compact stars with new NICER mass-radius measurements

TL;DR

This paper uses Bayesian methods to constrain covariant density functional models of dense matter in compact stars, incorporating new NICER mass-radius measurements to refine model parameters and improve understanding of neutron star interiors.

Contribution

It introduces a Bayesian framework to constrain density-dependent covariant density functional models using recent NICER mass-radius data, enhancing previous model assessments.

Findings

Tighter constraints on model parameters due to new observational data

Improved consistency between models and astrophysical measurements

Refined density dependence in nuclear matter equations of state

Abstract

Recent advancements in astrophysical observations of compact stars, particularly the new and updated NICER constraints, have provided mass-radius ($M$-$R$) data for pulsars spanning masses from 1 to $2\,M_{\odot }$. These data offer a unique opportunity to test modern theories of dense matter using multi-messenger constraints. Covariant density functional (CDF) models of nuclear matter, which capture a broad range of nuclear and astrophysical phenomena, provide a robust theoretical framework to interpret these observations. This study applies the Bayesian framework to a class of CDF models with density-dependent meson-nucleon couplings, specifically those based on nucleonic degrees of freedom. By incorporating the latest multi-messenger constraints, we impose tighter limits on the parameter space of these models and assess their consistency with observational data. Our analysis advances previous efforts by refining the density-dependence parameterization and integrating recent $M$-$R$ ellipses. This enables more stringent evaluations of dense matter models in light of new astrophysical observations.