A New Bayesian Framework with Natural Priors to Constrain the Neutron Star Equation of State

TL;DR

This paper introduces a Bayesian approach that directly parameterizes neutron star mass-radius data to improve EOS inference, offering broader coverage, efficiency, and reduced prior dependence.

Contribution

It presents a novel Bayesian framework that parameterizes in mass-radius space, enhancing robustness and computational efficiency over traditional pressure-energy density methods.

Findings

Broader coverage of physically allowed mass-radius space.

Enhanced computational efficiency.

Reduced dependence on prior assumptions.

Abstract

We propose a new Bayesian framework to infer the neutron star equation of state (EOS) from mass and radius observations and neutron matter theory by defining priors that directly parameterize mass-radius space instead of pressure-energy density space. We use direct and accurate inversion approximations to map mass-radius relations to the underlying EOS. We systematically compare its EOS inferences with those inferred from traditional EOS parameterizations, taking care to quantify the systematic prior uncertainties of both. Our results show that prior uncertainties should be included in all Bayesian approaches. The more natural alternative framework provides broader coverage of the physically allowed mass-radius space, especially small radius configurations, and yields enhanced computational efficiency and substantially reduced dependence on prior choices. Our results demonstrate that direct parameterization in observed space offers a robust and efficient alternative to traditional methods.