Generalised description of Neutron Star matter with nucleonic Relativistic Density Functional

TL;DR

This paper develops a relativistic density functional-based meta-modeling approach to describe neutron star matter, exploring uncertainties in nuclear parameters and constraining models with astrophysical data, especially GW170817.

Contribution

It introduces a novel meta-modelling technique for nuclear matter using density-dependent relativistic density functional, incorporating uncertainties and astrophysical constraints.

Findings

Constraint on tidal deformability from GW170817 is most restrictive.

Variation in $K_{sym}$ significantly affects neutron star composition.

Selected models with extreme $K_{sym}$ values are available in the extsc{CompOSE} database.

Abstract

In this work, we propose a meta-modelling technique to nuclear matter on the basis of a relativistic density functional with density-dependent couplings. Identical density dependence for the couplings both in the isoscalar and isovector sectors is employed. We vary the coupling parameters of the model to capture the uncertainties of the empirical nuclear matter parameters at saturation. Then, we construct a large ensemble of unified equations of state in a consistent manner both for clusterized and uniform matter in $\beta$-equilibrium at zero temperature. Finally, we calculate neutron star properties to check the consistency with astrophysical observations within a Bayesian framework. Out of the different sets of astrophysical data employed, constraint on tidal deformability from the GW170817 event was found to be the most stringent in the posteriors of different neutron star properties explored in the present study. We demonstrate in detail the impact of the isovector incompressibility ($K_{sym}$) on high-density matter that leads to a considerable variation in the composition of neutron star matter. A couple of selected models with extreme values of $K_{sym}$, which satisfy various modern nuclear physics and neutron star astrophysics constraints, are uploaded in the \textsc{CompOSE} \cite{Typel:2013rza} database for use by the community.