Bayesian refinement of covariant energy density functionals

TL;DR

This paper uses Bayesian methods to refine covariant energy density functionals for neutron-rich matter, integrating recent observational and experimental constraints to improve model accuracy, especially regarding neutron star properties.

Contribution

It introduces a Bayesian framework that incorporates recent multi-messenger astrophysical data to refine covariant energy density functionals for neutron-rich matter.

Findings

Refined models better match neutron star mass and radius constraints.

Significant improvements in symmetry energy predictions.

Persistent difficulty in matching neutron skin thickness measurements.

Abstract

The last five years have seen remarkable progress in our quest to determine the equation of state of neutron rich matter. Recent advances across the theoretical, experimental, and observational landscape have been incorporated in a Bayesian framework to refine existing covariant energy density functionals previously calibrated by the properties of finite nuclei. In particular, constraints on the maximum neutron star mass from pulsar timing, on stellar radii from the NICER mission, on tidal deformabilities from the LIGO-Virgo collaboration, and on the dynamics of pure neutron matter as predicted from chiral effective field theories, have resulted in significant refinements to the models, particularly to those predicting a stiff symmetry energy. Still, even after these improvements, we find challenging to reproduce simultaneously the neutron skin thickness of both ${}^{208}$Pb and ${}^{48}$Ca recently reported by the PREX/CREX collaboration.