Optimizing Bayesian model selection for equation of state of cold neutron stars

TL;DR

This paper presents BEOMS, a computational framework for Bayesian model selection of neutron star equations of state using gravitational wave data, demonstrating improved efficiency and accuracy in distinguishing models with fewer events.

Contribution

Introduction of BEOMS, a novel Bayesian evidence calculation framework optimized for neutron star EOS model selection with gravitational wave observations.

Findings

Model selection is most effective in the mass and tidal deformability subspace.

Fewer gravitational wave events are needed for high-confidence model discrimination.

Measurement precision of tidal deformability impacts model selection accuracy.

Abstract

We introduce a computational framework, Bayesian Evidence calculation fOr Model Selection (BEOMS) to evaluate multiple Bayesian model selection methods in the context of determining the equation of state (EOS) for cold neutron star (NS), focusing on their performance with current and next-generation gravitational wave (GW) observatories. We conduct a systematic comparison of various EOS models by using posterior distributions obtained from EOS-agnostic Bayesian inference of binary parameters applied to GWs from a population of binary neutron star (BNS) mergers. The cumulative evidence for each model is calculated in a multi-dimensional parameter space characterized by neutron star masses and tidal deformabilities. Our findings indicate that Bayesian model selection is most effective when performed in the two-dimensional subspace of component mass and tidal deformability, requiring fewer events to distinguish between EOS models with high confidence. Furthermore, we establish a relationship between the precision of tidal deformability measurements and the accuracy of model selection, taking into account the evolving sensitivities of current and planned GW observatories. BEOMS offers computational efficiency and can be adapted to execute model selection for gravitational wave data from other sources.