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

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.