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The discovery of the coalescence of binary neutron star GW170817 was awatershed moment in the field of gravitational wave astronomy. Among the richvariety of information that we were able to uncover from this discovery was thefirst non-electromagnetic measurement of the neutron star radius, and the coldnuclear equation of state. It also led to a large equation of statemodel-selection study from gravitational-wave data. In those studies Bayesiannested sampling runs were conducted for each candidate equation of state modelto compute their evidence in the gravitational-wave data. Such studies, thoughinvaluable, are computationally expensive and require repeated, redundant,computation for any new models. We present a novel technique to conductmodel-selection of equation of state in an extremely rapid fashion (~minutes)on any arbitrary model. We test this technique against the results of anested-sampling model-selection technique published earlier by the LIGO/Virgocollaboration, and show that the results are in good agreement with a medianfractional error in Bayes factor of about 10%, where we assume that the trueBayes factor is calculated in the aforementioned nested sampling runs. We foundthat the highest fractional error occurs for equation of state models that havevery little support in the posterior distribution, thus resulting in largestatistical uncertainty. We then used this method to combine multiple binaryneutron star mergers to compute a joint-Bayes factor between equation of statemodels. This is achieved by stacking the evidence of the individual events andcomputing the Bayes factor from these stacked evidences for each pairs ofequation of state.

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