A Physics Informed Bayesian Neural Network for the Neutron Star Equation of State

arXiv:2604.24949 · astro-ph.HE, nucl-th · Submitted 2026-04-27 · Read on arXiv

astro-ph.HE, nucl-th

Submitted: 2026-04-27

Updated: 2026-09-15

Comments: Accepted in PRD

License: http://creativecommons.org/licenses/by-nc-sa/4.0/

The gist: We present a physics-informed Bayesian neural-network framework for inferring neutron-star equations of state from theoretical priors and propagating the resulting uncertainty to stellar observables.

Terminology

Abstract

We present a physics-informed Bayesian neural-network framework for inferring neutron-star equations of state from theoretical priors and propagating the resulting uncertainty to stellar observables. Trained on a representative set of hadronic EoSs, the model learns the equation of state through stochastic variational inference by representing the squared speed of sound with a bounded network output and obtaining the pressure by integration, so that causality, thermodynamic stability, and monotonicity are guaranteed by construction, with low-density nuclear and perturbative-QCD normalization anchors. Core EoSs are matched to an SLy4 crust and propagated through a unified Tolman-Oppenheimer-Volkoff-plus-tidal solver to obtain posterior predictions in the mass-radius (M - R) and mass-tidal-deformability (M - Λ) planes. The physics-informed prior is then updated with current multi-messenger data: NICER radius measurements, the GW170817 tidal-deformability constraint, and the 2,M maximum-mass bound; included directly in the variational objective. The observational update moves the canonical radius from R 1.4=13.41, km in the prior to R 1.4=12.74+0.97-0.73, km (nominal 90% variational CI), with Λ 1.4=428+249-130 and M max 2.0,M. This framework provides a non-parametric route from microphysical EoS uncertainty to neutron-star observables.

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