Microscopic constraints for the equation of state and structure of neutron stars: a Bayesian model mixing framework

TL;DR

This paper develops a Bayesian model mixing framework using Gaussian processes to combine microscopic theories and experimental data, constraining the neutron star matter equation of state and predicting mass-radius relations.

Contribution

It extends Bayesian model mixing to asymmetric matter and introduces non-stationary kernels for incorporating diverse data sources in neutron star EOS modeling.

Findings

Constraints on neutron star EOS from chiral EFT and pQCD theories.

Demonstrated use of non-stationary kernels for theory and experimental data.

Produced neutron star mass-radius relations with quantified uncertainties.

Abstract

Bayesian model mixing (BMM) is a statistical technique that can combine constraints from different regions of an input space in a principled way. Here we extend our BMM framework for the equation of state (EOS) of strongly interacting matter from symmetric nuclear matter to asymmetric matter, specifically focusing on zero-temperature, charge-neutral, $\beta$-equilibrated matter. We use Gaussian processes (GPs) to infer constraints on the neutron star matter EOS at intermediate densities from two different microscopic theories: chiral effective field theory ($\chi$EFT) at baryon densities around nuclear saturation, $n_B \sim n_0$, and perturbative QCD at asymptotically high baryon densities, $n_B \geqslant 20 n_0$. The uncertainties of the $\chi$EFT and pQCD EOSs are obtained using the BUQEYE truncation error model. We demonstrate the flexibility of our framework through the use of two categories of GP kernels: conventional stationary kernels and a non-stationary changepoint kernel. We use the latter to explore potential constraints on the dense matter EOS by including exogenous data representing theory predictions and heavy-ion collision measurements at densities $\geqslant 2n_0$. We also use our EOSs to obtain neutron star mass-radius relations and their uncertainties. Our framework, whose implementation will be available through a GitHub repository, provides a prior distribution for the EOS that can be used in large-scale neutron-star inference frameworks.