Multi-Physics Bayesian Analysis of Neutron Star Crust Using Relativistic Mean-Field Model
Vishal Parmar, Ignazio Bombaci

TL;DR
This paper employs a Bayesian approach with a relativistic mean-field model to analyze neutron-star crust properties, integrating nuclear data and multimessenger observations to constrain key crustal parameters and assess systematic uncertainties.
Contribution
It introduces a unified Bayesian framework for neutron-star crust analysis using RMF models, incorporating diverse data sources and evaluating systematic effects of crust-core matching.
Findings
Crust-core transition density is mainly influenced by symmetry-energy slope and curvature.
Transition pressure significantly affects global crustal properties.
Matched crust-core models can cause systematic differences in neutron-star predictions.
Abstract
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…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Nuclear physics research studies · High-Energy Particle Collisions Research
