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BayesWave analysis pipeline in the era of gravitational wave observations

Cornish, Neil J. and Littenberg, Tyson B. and Bécsy, Bence and Chatziioannou, Katerina and Clark, James A. and Ghonge, Sudarshan and Millhouse, Margaret (2021) BayesWave analysis pipeline in the era of gravitational wave observations. Physical Review D, 103 (4). Art. No. 044006. ISSN 2470-0010. doi:10.1103/PhysRevD.103.044006.

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We describe updates and improvements to the BayesWave gravitational wave transient analysis pipeline, and provide examples of how the algorithm is used to analyze data from ground-based gravitational wave detectors. BayesWave models gravitational wave signals in a morphology-independent manner through a sum of frame functions, such as Morlet-Gabor wavelets or chirplets. BayesWave models the instrument noise using a combination of a parametrized Gaussian noise component and nonstationary and non-Gaussian noise transients. Both the signal model and noise model employ trans-dimensional sampling, with the complexity of the model adapting to the requirements of the data. The flexibility of the algorithm makes it suitable for a variety of analyses, including reconstructing generic unmodeled signals; cross-checks against modeled analyses for compact binaries; as well as separating coherent signals from incoherent instrumental noise transients (glitches). The BayesWave model has been extended to account for gravitational wave signals with generic polarization content and the simultaneous presence of signals and glitches in the data. We describe updates in the BayesWave prior distributions, sampling proposals, and burn-in stage that provide significantly improved sampling efficiency. We present standard review checks indicating the robustness and convergence of the BayesWave trans-dimensional sampler.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Cornish, Neil J.0000-0002-7435-0869
Littenberg, Tyson B.0000-0002-9574-578X
Bécsy, Bence0000-0003-0909-5563
Chatziioannou, Katerina0000-0002-5833-413X
Ghonge, Sudarshan0000-0002-5476-938X
Millhouse, Margaret0000-0002-8659-5898
Alternate Title:The BayesWave analysis pipeline in the era of gravitational wave observations
Additional Information:© 2021 American Physical Society. Received 19 November 2020; accepted 14 January 2021; published 2 February 2021. This research has made use of data, software and/or web tools obtained from the Gravitational Wave Open Science Center [45], a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. LIGO is funded by the U.S. National Science Foundation. 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. N. J. C. and B. B. acknowledge the support of NSF Grants No. PHY1607343 and No. PHY1912053. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants No. PHY-0757058 and No. PHY-0823459, and for resources provided by the Open Science Grid [46,47], which is supported by the National Science Foundation Grant No. 1148698, and the U.S. Department of Energy’s Office of Science. The Flatiron Institute is supported by the Simons Foundation. The J. A. C. and S. G. gratefully acknowledge the NSF for financial support from Grants No. PHY 1806580, No. PHY 1809572, and No. TG-PHY120016. Parts of this research were conducted by the Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav), through Project No. CE170100004.
Funding AgencyGrant Number
Open Science GridUNSPECIFIED
Department of Energy (DOE)UNSPECIFIED
Simons FoundationUNSPECIFIED
Australian Research CouncilCE170100004
Centre National de la Recherche Scientifique (CNRS)UNSPECIFIED
Istituto Nazionale di Fisica Nucleare (INFN)UNSPECIFIED
Issue or Number:4
Record Number:CaltechAUTHORS:20210111-160845464
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107406
Deposited By: George Porter
Deposited On:12 Jan 2021 16:29
Last Modified:02 Sep 2021 20:29

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