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Bayesian inference for compact binary coalescences with BILBY: Validation and application to the first LIGO-Virgo gravitational-wave transient catalogue

Romero-Shaw, I. M. and Talbot, C. and Biscoveanu, S. and D'Emilio, V. and Ashton, G. and Berry, C. P. L. and Coughlin, S. and Galaudage, S. and Hoy, C. and Huebner, M. and Phukon, K. S. and Pitkin, M. and Rizzo, M. and Sarin, N. and Smith, R. and Stevenson, S. and Vajpeyi, A. and Arene, M. and Athar, K. and Banagiri, S. and Bose, N. and Carney, M. and Chatziioannou, K. and Cotesta, R. and Edelman, B. and Garcia-Quiros, C. and Ghosh, Abhirup and Green, R. and Haster, C. -J. and Kim, A. X. and Hernandez-Vivanco, F. and Magana Hernandez, I. and Karathanasis, C. and Lasky, P. D. and De Lillo, N. and Lower, M. E. and Macleod, D. and Mateu-Lucena, M. and Miller, A. and Millhouse, M. and Morisaki, S. and Oh, S. H. and Ossokine, S. and Payne, E. and Powell, J. and Puerrer, M. and Ramos-Buades, A. and Raymond, V. and Thrane, E. and Veitch, J. and Williams, D. and Williams, M. J. and Xiao, L. (2020) Bayesian inference for compact binary coalescences with BILBY: Validation and application to the first LIGO-Virgo gravitational-wave transient catalogue. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200729-071950663

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Abstract

Gravitational waves provide a unique tool for observational astronomy. While the first LIGO--Virgo catalogue of gravitational-wave transients (GWTC-1) contains eleven signals from black hole and neutron star binaries, the number of observations is increasing rapidly as detector sensitivity improves. To extract information from the observed signals, it is imperative to have fast, flexible, and scalable inference techniques. In a previous paper, we introduced BILBY: a modular and user-friendly Bayesian inference library adapted to address the needs of gravitational-wave inference. In this work, we demonstrate that BILBY produces reliable results for simulated gravitational-wave signals from compact binary mergers, and verify that it accurately reproduces results reported for the eleven GWTC-1 signals. Additionally, we provide configuration and output files for all analyses to allow for easy reproduction, modification, and future use. This work establishes that BILBY is primed and ready to analyse the rapidly growing population of compact binary coalescence gravitational-wave signals.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2006.00714arXivDiscussion Paper
ORCID:
AuthorORCID
Chatziioannou, K.0000-0002-5833-413X
Xiao, L.0000-0003-2703-449X
Additional Information:This work is supported through Australian Research Council (ARC) Centre of Excellence CE170100004. PDL is supported through ARC Future Fellowship FT160100112 and ARC Discovery Project DP180103155. ET is supported through ARC Future Fellowship FT150100281 and CE170100004. This work is partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2019R1A2C2006787). NB acknowledges Inspire division, DST, Government of India for the fellowship support. This work is partially supported by the National Science Foundation under Grant No. PHY-1912648. SB, C-JH., and CT acknowledge support of the National Science Foundation, and the LIGO Laboratory. SB is also supported by the Paul and Daisy Soros Fellowship for New Americans and the NSF Graduate Research Fellowship under Grant No. DGE-1122374. This work used Bilby = v0.6.9, bilby_pipe = v0.3.12, dynesty = v1.0.1, LALSuite =v6.49, PESummary = v0.5.6 This research has made use of data, software and/or web tools obtained from the Gravitational Wave Open Science Center (Abbott et al. 2019e), a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. Computing was performed on the OzSTAR Australian national facility at Swinburne University of Technology, which receives funding in part from the Astronomy National Collaborative Research Infrastructure Strategy (NCRIS) allocation provided by the Australian Government, LIGO Laboratory computing clusters at California Institute of Technology and LIGO Hanford Observatory supported by National Science Foundation Grants PHY-0757058 and PHY-0823459, and the Quest computing cluster, which is jointly supported by the Office of the Provost, the Office for Research and Northwestern University Information Technology, and funded by the National Science Foundation under Grant No. PHY-1726951. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation and operates under cooperative agreement PHY-1764464.Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale della Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by Polish and Hungarian institutes.
Group:LIGO
Funders:
Funding AgencyGrant Number
Australian Research CouncilCE170100004
Australian Research CouncilFT160100112
Australian Research CouncilDP180103155
Australian Research CouncilFT150100281
Australian Research CouncilCE170100004
National Research Foundation of Korea2019R1A2C2006787
Department of Science and Technology (India)UNSPECIFIED
NSFPHY-1912648
LIGO LaboratoryUNSPECIFIED
Paul and Daisy Soros FellowshipUNSPECIFIED
NSF Graduate Research FellowshipDGE-1122374
NSFPHY-0757058
NSFPHY-0823459
Northwestern UniversityUNSPECIFIED
NSFPHY-1726951
NSFPHY-1764464
Centre National de Recherche Scientifique (CNRS)UNSPECIFIED
Istituto Nazionale di Fisica Nucleare (INFN)UNSPECIFIED
NikhefUNSPECIFIED
Subject Keywords:gravitational waves – stars: neutron – stars: black holes – methods: data analysis – transients: black hole mergers – transients: neutron star mergers
Record Number:CaltechAUTHORS:20200729-071950663
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200729-071950663
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104628
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:29 Jul 2020 18:16
Last Modified:29 Jul 2020 18:16

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