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 - Funding Information:
The authors will like to thank Reed Essick for meticulously reading through the manuscript and reviewing the code. The authors will also like to acknowledge Katerina Chatziioannou for reviewing the posterior samples and the parameter estimation analysis that was run to generate them. The authors will also like to thank Tim Dietrich for his valuable suggestion to improve the scientific content of the paper, and for conducting the internal review of the article. S. G., X. L., and J. C. will like to acknowledge NSF Grant No. NSF PHY-1912649 that supported this work. Large fraction of the analysis of the data was performed on the Nemo cluster at the Leonard E. Parker Center for Gravitation, Cosmology and Astrophysics at the University of Wisconsin-Milwaukee, CIT cluster at Caltech, LHO cluster at the Hanford LIGO Observatory, and the LLO cluster at the Livingston LIGO Observatory operated by the LIGO Lab, supported by the NSF Grants No. PHY-1626190, No. PHY-1700765, No. PHY-0757058 and No. PHY-0823459. This research has made use of data, software and/or web tools obtained from the Gravitational Wave Open Science Center , a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. LIGO Laboratory and Advanced LIGO are funded by the United States National Science Foundation (NSF) as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. This material is based upon work supported by NSFs LIGO Laboratory which is a major facility fully funded by the National Science Foundation.
Funding Information:
National Science Foundation University of Wisconsin-Milwaukee Science and Technology Facilities Council Max-Planck-Gesellschaft State of Niedersachsen Australian Research Council European Gravitational Observatory Centre National de la Recherche Scientifique Instituto Nazionale di Fisica Nucleare Dutch Nikhef
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 -