Prior probability distributions of neutron star crust models

TL;DR

This paper develops probability distributions for neutron star crust models using nuclear data, assesses uncertainties, and explores correlations to improve understanding of crust composition and structure.

Contribution

It introduces a consistent probabilistic framework for neutron star crust modeling, incorporating nuclear data and quantifying model uncertainties and correlations.

Findings

Over 50% of the crust by mass is pasta phase.

Crust composition varies with density, with specific ranges provided.

Correlations between crust properties and nuclear parameters are quantified.

Abstract

To make best use of multi-faceted astronomical and nuclear data-sets, probability distributions of neutron star models that can be used to propagate errors consistently from one domain to another are required. We take steps toward a consistent model for this purpose, highlight where model inconsistencies occur and assess the resulting model uncertainty. Using two distributions of nuclear symmetry energy parameters - one uniform, the other based on pure neutron matter theory, we prepare two ensembles of neutron star inner crust models. We use an extended Skyrme energy-density functional within a compressible liquid drop model (CLDM). We fit the surface parameters of the CLDM to quantum 3D Hartree-Fock calculations of crustal nuclei. All models predict more than 50% of the crust by mass and 15% of the crust by thickness comprises pasta with medians of around 62% and 30% respectively. We also present 68% and 95% ranges for the crust composition as a function of density. We examine the relationships between crust-core boundary and pasta transition properties, the thickness of the pasta layers, the symmetry energy at saturation and sub-saturation densities and the neutron skins of 208Pb and 48Ca. We quantify the correlations using the maximal information coefficient, which can effectively characterize non-linear relationships. Future measurements of neutron skins, information from nuclear masses and giant resonances, and theoretical constraints on PNM will be able to place constraints on the location of the pasta and crust-core boundaries and the amount of pasta in the crust.