Inverse-mapped density-dependent relativistic mean-field inference of the neutron-star equation of state with multi-messenger constraints
Wen-Jie Xie, Cheng-Jun Xia

TL;DR
This paper develops a Bayesian framework to infer the neutron-star equation of state using a density-dependent relativistic mean-field model constrained by multi-messenger astrophysical data, revealing insights into neutron-star matter properties.
Contribution
It introduces an inverse-mapping procedure for reconstructing density-dependent couplings in the DD-RMF model, integrating diverse observational constraints for the first time.
Findings
Chiral EFT favors a soft symmetry-energy slope (~38 MeV)
Heavy-ion data suggests intermediate-density softness
Massive pulsar data requires high-density stiffness
Abstract
We perform a Bayesian inference of the equation of state (EOS) of cold dense matter within a density-dependent relativistic mean-field (DD-RMF) model. An explicit inverse-mapping procedure reconstructs the density-dependent couplings from a physically interpretable ten-dimensional parameter set while enforcing thermodynamic consistency together with stability and causality conditions. The EOS is constrained by complementary multi-messenger data including chiral effective field theory calculations at low density, heavy-ion collision flow information at intermediate densities, NICER mass-radius posteriors, and the existence of approximately two-solar-mass pulsars. The combined constraints strongly restrict both isoscalar and isovector sectors. In particular, the chiral effective field theory band favors a relatively soft symmetry-energy slope around 38 MeV, corresponding to a compact…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · High-Energy Particle Collisions Research · Scientific Research and Discoveries
