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

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.