Bayesian inference of neutron star crust properties using an ab initio-benchmarked meta-model

TL;DR

This paper improves neutron star crust modeling by integrating ab initio calculations into a Bayesian framework, reducing uncertainties and enhancing the interpretation of multimessenger astrophysical data.

Contribution

It introduces a refined meta-modeling approach that incorporates low-density corrections based on microscopic calculations, advancing the accuracy of neutron star crust property predictions.

Findings

Reduced uncertainties in crust-core transition density and pressure

Enhanced understanding of crustal composition and moment of inertia

Framework applicable to various EoS models for future data interpretation

Abstract

Accurate modeling of the neutron star crust is essential for interpreting multimessenger observations and constraining the nuclear equation of state (EoS). However, standard phenomenological EoS models often rely on heuristic extrapolations in the low-density regime, which are inconsistent with microscopic predictions. In this work, we refine a unified meta-modeling framework for the EoS by incorporating low-density corrections based on energy density functionals constrained by ab initio neutron-matter calculations. Using Bayesian inference to combine information from astrophysical observations, nuclear theory, and experiments, we assess the impact of these corrections on key crustal properties, including the crust-core transition density and pressure, crustal composition, and moment of inertia. The improved model reduces uncertainties in the inner crust and emphasizes the importance of low-density physics in EoS modeling, highlighting the value of integrating both theoretical and observational constraints across densities to robustly describe the EoS. Moreover, the adopted approach can be readily applied to any existing EoS model to provide a solid framework for interpreting upcoming high-precision multimessenger data.