Properties of the neutron star crust informed by nuclear structure data

TL;DR

This paper uses Bayesian analysis with nuclear physics data to model neutron star crust properties, linking nuclear structure to astrophysical observations.

Contribution

It introduces a fully Bayesian, consistent approach to connect nuclear matter parameters with neutron star crust and global properties.

Findings

Increased neutron star surface thickness and crustal moment of inertia.

First fully Bayesian treatment of correlations among nuclear matter parameters.

Consistent modeling of neutron star crust using nuclear physics data.

Abstract

We perform a Bayesian analysis of the neutron star (NS) equation of state (EoS) based on a wide set of Skyrme functionals, derived from previous nuclear physics inferences. The novelty of this approach lies in starting from the full multidimensional posterior distribution of nuclear matter parameters, consistent with a comprehensive set of static and dynamic nuclear structure observables. We construct unified EoSs for $npe\mu$ matter, where the inner crust of the NS is treated using an extended Thomas-Fermi method, providing for the first time a fully consistent Bayesian treatment of the correlation of bulk with surface as well as with spin-orbit and effective mass parameters. We then employ a standard Bayesian framework to identify those EoSs that satisfy astrophysical constraints from NS mass measurements, the tidal deformability from GW170817, and NICER mass-radius observations. We also examine NS observables, such as the crustal moment of inertia, which is crucial in understanding pulsar glitches. Compared to previous works, we observe an increase in both the NS surface thickness and the crustal moment of inertia.