Constrained Gaussian-process bridge prior for neutron-star equation-of-state inference

TL;DR

This paper introduces a novel, model-agnostic Gaussian-process-based method for neutron star equation-of-state inference that incorporates thermodynamic constraints and multiple theoretical inputs efficiently.

Contribution

It generalizes Gaussian processes to include thermodynamic constraints, enabling flexible, stable, and consistent nonparametric priors for neutron star EOS inference.

Findings

Allows inclusion of multiple training points with thermodynamic consistency.

Enables tuning between conservative and theory-informed priors.

Integrates chiral EFT and QCD constraints within a unified framework.

Abstract

We set forth a new method for generating model-agnostic, nonparametric priors for neutron star equation-of-state inference that are stable, causal and thermodynamically consistent by construction. This generalizes Gaussian processes to include global thermodynamic constraints, specifically allowing the inclusion of any number of training points in the form $(\mu, n, p)$ while retaining thermodynamic consistency between them. The method is based on constructing constrained Gaussian-process bridges, whose correlation properties can be tuned at will allowing flexibility between a conservative prior and a theory-informed prior. The method does not require any shooting to obey multiple constraints and provides an efficient and informed way to include both chiral effective field theory and perturbative quantum chromodynamics constraints within the same framework.