TY - JOUR

T1 - Rapid model comparison of equations of state from gravitational wave observation of binary neutron star coalescences

AU - Ghosh, Shaon

AU - Liu, Xiaoshu

AU - Creighton, Jolien

AU - Hernandez, Ignacio Magaña

AU - Kastaun, Wolfgang

AU - Pratten, Geraint

N1 - Publisher Copyright:
© 2021 American Physical Society

PY - 2021/10/15

Y1 - 2021/10/15

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85116349106&partnerID=8YFLogxK

U2 - 10.1103/PhysRevD.104.083003

DO - 10.1103/PhysRevD.104.083003

M3 - Article

AN - SCOPUS:85116349106

SN - 2470-0010

VL - 104

JO - Physical Review D

JF - Physical Review D

IS - 8

M1 - 083003

ER -