Searching for phase transitions in neutron stars with modified Gaussian processes

TL;DR

This paper extends Gaussian process models to include complex features in the neutron star equation of state, integrating diverse observational data to explore phase transition signatures.

Contribution

It introduces a Bayesian Gaussian process framework capable of modeling nontrivial speed of sound features in neutron star matter, informed by multi-messenger observations.

Findings

Features like bumps and kinks are compatible with current data.

Inclusion of features affects the EoS posterior distributions.

Bayesian analysis constrains possible phase transition signatures.

Abstract

Gaussian processes provide a promising framework by which to extrapolate the equation of state (EoS) of cold, catalyzed matter beyond $1-2$ times nuclear saturation density. Here we discuss how to extend Gaussian processes to include nontrivial features in the speed of sound, such as bumps, kinks, and plateaus, which are predicted by nuclear models with exotic degrees of freedom. Using a fully Bayesian analysis incorporating measurements from X-ray sources, gravitational wave observations, and perturbative QCD results, we show that these features are compatible with current constraints and report on how the features affect the EoS posteriors.