Bayesian inferences on covariant density functionals from multimessenger astrophysical data: Influences of parametrizations of density dependent couplings
Guo-Jun Wei, Jia-Jie Li, Armen Sedrakian, Yong-Jia Wang, Qing-Feng Li, and Fu-Hu Liu

TL;DR
This study uses Bayesian methods to analyze how different density dependence parametrizations in covariant density functionals influence the modeling of dense nuclear matter and neutron stars, highlighting sensitivities at high densities.
Contribution
It introduces a rational-function parametrization constrained by multimessenger astrophysical data, exploring the impact of functional form choices on dense matter properties.
Findings
Different parametrizations yield similar inferences but differ significantly at suprasaturation densities.
Allowing free adjustment of $Q_{sat}$ improves modeling flexibility for nuclear and neutron star matter.
Extending freedom to $K_{sym}$ captures variations in symmetry energy at high densities.
Abstract
Covariant density functionals have been successfully applied to the description of finite nuclei and dense nuclear matter. These functionals are often constructed by introducing density dependence into the nucleon-meson couplings, typically through functions that depend only on the vector, i.e., proper baryon density. In this work, we employ a Bayesian framework to investigate how different parametrizations, characterized by distinct functional forms and by their dependencies on vector and scalar densities, affect the properties of dense matter and compact stars. Our analysis demonstrates that although all considered parametrizations yield broadly comparable inferences, the differences in the equation of state and the symmetry energy remain significant at suprasaturation densities, reflecting the sensitivity to the chosen functional form of the density dependence. We find that allowing…
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