Rapid model comparison of equations of state from gravitational wave observation of binary neutron star coalescences
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 nonelectromagnetic 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 gravitationalwave data. In those studies Bayesian nested sampling runs were conducted for each candidate equation of state model to compute their evidence in the gravitationalwave 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 nestedsampling 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 jointBayes 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.
 Publication:

Physical Review D
 Pub Date:
 October 2021
 DOI:
 10.1103/PhysRevD.104.083003
 arXiv:
 arXiv:2104.08681
 Bibcode:
 2021PhRvD.104h3003G
 Keywords:

 General Relativity and Quantum Cosmology;
 Astrophysics  High Energy Astrophysical Phenomena
 EPrint:
 Dataset produced in this study https://zenodo.org/record/4679013#.YHuUnJNKhsA