Multi-Physics Bayesian Analysis of Neutron Star Crust Using Relativistic Mean-Field Model

We study the properties of neutron-star crust within a Bayesian framework based on a unified relativistic mean-field (RMF) description of dense matter. The analysis focuses on the posterior distributions of crust properties, constrained by nuclear experimental data, chiral effective field theory, and multimessenger neutron-star observations. In the inference, the outer crust is fixed using the AME2020 nuclear mass table, supplemented by Hartree–Fock–Bogoliubov mass models, while the inner crust is described using a compressible liquid-drop model consistently coupled to the RMF interaction. The same RMF framework is used to describe the uniform core, ensuring a unified treatment across all density regimes. From the resulting posteriors, we extract key crustal observables, including the crust–core transition density and pressure, crust thickness, crust mass, and the fractional crustal moment of inertia. We find that the transition density is primarily governed by the symmetry-energy slope $L$ and curvature $K_{\mathrm{s}\mathrm{y}\mathrm{m}}$ evaluated at sub-saturation densities, while the transition pressure plays a central role in determining global crustal properties. The inner-crust equation of state reflects a collective interplay between isovector nuclear-matter properties rather than a dependence on any single parameter. We also assess the impact of using matched crust–core constructions and show that they can introduce systematic differences in predicted neutron-star properties when compared with fully unified treatments.