- Open Access
Equation of state extrapolation systematics: Parametric vs nonparametric inference of neutron star structure
Phys. Rev. D 113, 123032 – Published 11 June, 2026
DOI: https://doi.org/10.1103/x12t-29d9
Abstract
The equation of state (EOS) of cold dense matter remains one of the central open problems in nuclear astrophysics. Its inference is complicated by the lack of ab initio theoretical control above about twice nuclear saturation density, where the EOS must be extrapolated. Parametric schemes such as piecewise polytropes (PP) are computationally efficient but impose restrictive functional forms, while nonparametric approaches such as Gaussian processes (GP) offer greater flexibility at the cost of larger prior volumes. In this work, we extend our hybrid EOS framework by replacing the high-density polytropic extension with a Gaussian process representation of the squared sound speed, anchored at low densities by the SLy crust EOS and a nuclear meta-model constrained by and laboratory measurements. Using a hierarchical Bayesian analysis, we jointly constrain the EOS and the neutron star mass distribution with multimessenger data, including NICER radius measurements, GW170817 and GW190425 tidal deformabilities, pulsars, and neutron skin experiments. We explore four scenarios defined by the choice of high-density extrapolation (PP vs GP) and hot spot geometry in the NICER modeling of PSR ( vs PDT-U). We find that GP extrapolations generally yield softer EOS posteriors with broader uncertainty bands. Hot spot geometry assumptions also play an important role, leading to systematic shifts in the inferred mass-radius relations. Bayesian evidence strongly favors the geometry over PDT-U across both extrapolation schemes, while the GP extension is consistently preferred over PP, with substantial support. Taken together, these results underscore the importance of both observational modeling choices and EOS extrapolation strategies in shaping neutron star EOS inferences, and demonstrate that a GP-based extension provides a robust framework for quantifying systematic uncertainties in high-density matter.
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