Rapid model comparison of equations of state from gravitational wave observation of binary neutron star coalescences

TL;DR

This paper introduces a rapid, computationally efficient method for comparing neutron star equations of state using gravitational wave data, significantly reducing analysis time while maintaining accuracy.

Contribution

The authors develop and validate a fast model-selection technique for equations of state, enabling quick analysis of gravitational wave data without extensive nested sampling.

Findings

The new method achieves ~10% median fractional error in Bayes factors compared to traditional nested sampling.

It can perform model comparison in minutes, vastly faster than previous approaches.

The technique successfully combines multiple events to evaluate equations of state jointly.

Abstract

The discovery of the coalescence of binary neutron star GW170817 was a watershed moment in the field of gravitational wave astronomy. Among the rich variety of information that we were able to uncover from this discovery was the first non-electromagnetic measurement of the neutron star radius, and the cold nuclear equation of state. It also led to a large equation of state model-selection study from gravitational-wave data. In those studies Bayesian nested sampling runs were conducted for each candidate equation of state model to compute their evidence in the gravitational-wave data. Such studies, though invaluable, are computationally expensive and require repeated, redundant, computation for any new models. We present a novel technique to conduct model-selection of equation of state in an extremely rapid fashion (~minutes) on any arbitrary model. We test this technique against the results of a nested-sampling model-selection technique published earlier by the LIGO/Virgo collaboration, and show that the results are in good agreement with a median fractional error in Bayes factor of about 10%, where we assume that the true Bayes factor is calculated in the aforementioned nested sampling runs. We found that the highest fractional error occurs for equation of state models that have very little support in the posterior distribution, thus resulting in large statistical uncertainty. We then used this method to combine multiple binary neutron star mergers to compute a joint-Bayes factor between equation of state models. This is achieved by stacking the evidence of the individual events and computing the Bayes factor from these stacked evidences for each pairs of equation of state.