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 $K_{\rm sym}$ 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.