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Parameter Estimation for Gravitational-wave Bursts with the BayesWave Pipeline

Bécsy, Bence and Raffai, Peter and Cornish, Neil J. and Essick, Reed and Kanner, Jonah and Katsavounidis, Erik and Littenberg, Tyson B. and Millhouse, Margaret and Vitale, Salvatore (2017) Parameter Estimation for Gravitational-wave Bursts with the BayesWave Pipeline. Astrophysical Journal, 839 (1). Art. No. 15. ISSN 0004-637X. PMCID PMC5927389. http://resolver.caltech.edu/CaltechAUTHORS:20170410-100436817

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Abstract

We provide a comprehensive multi-aspect study of the performance of a pipeline used by the LIGO-Virgo Collaboration for estimating parameters of gravitational-wave bursts. We add simulated signals with four different morphologies (sine-Gaussians (SGs), Gaussians, white-noise bursts, and binary black hole signals) to simulated noise samples representing noise of the two Advanced LIGO detectors during their first observing run. We recover them with the BayesWave (BW) pipeline to study its accuracy in sky localization, waveform reconstruction, and estimation of model-independent waveform parameters. BW localizes sources with a level of accuracy comparable for all four morphologies, with the median separation of actual and estimated sky locations ranging from 25°.1 to 30°.3. This is a reasonable accuracy in the two-detector case, and is comparable to accuracies of other localization methods studied previously. As BW reconstructs generic transient signals with SG wavelets, it is unsurprising that BW performs best in reconstructing SG and Gaussian waveforms. The BW accuracy in waveform reconstruction increases steeply with the network signal-to-noise ratio (S/N_(net), reaching a 85% and 95% match between the reconstructed and actual waveform below S/N_(net) ≈ 20 and S/N_(net) ≈ 50, respectively, for all morphologies. The BW accuracy in estimating central moments of waveforms is only limited by statistical errors in the frequency domain, and is also affected by systematic errors in the time domain as BW cannot reconstruct low-amplitude parts of signals that are overwhelmed by noise. The figures of merit we introduce can be used in future characterizations of parameter estimation pipelines.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3847/1538-4357/aa63efDOIArticle
http://iopscience.iop.org/article/10.3847/1538-4357/aa63ef/meta#PublisherArticle
https://arxiv.org/abs/1612.02003arXivDiscussion Paper
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5927389/PubMed CentralArticle
ORCID:
AuthorORCID
Kanner, Jonah0000-0001-8115-0577
Additional Information:© 2017 American Astronomical Society. Received 2016 December 2; revised 2017 February 27; accepted 2017 February 27; published 2017 April 7. This paper was reviewed by the LIGO Scientific Collaboration under LIGO Document P1600181. We thank Marco Drago and Sergey Klimenko for their valuable comments on the manuscript. We acknowledge the Burst First2Years sky localization Open Data release. The authors acknowledge the support of the National Science Foundation and the LIGO Laboratory. 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-0757058. The authors would like to acknowledge the use of the LIGO Data Grid computer clusters for performing all the computation reported in the paper. Bence Bécsy was supported by the ÚNKP-16-2 New National Excellence Program of the Ministry of Human Capacities. Bence Bécsy was supported by the Hungarian Templeton Program that was made possible through the support of a grant from the Templeton World Charity Foundation, Inc. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Templeton World Charity Foundation, Inc. Peter Raffai is grateful for the support of the Hungarian Academy of Sciences through the "Bolyai János" Research Scholarship programme.
Group:LIGO
Funders:
Funding AgencyGrant Number
NSFPHY-0757058
Ministry of Human Capacities (Hungary)ÚNKP-16-2 New National Excellence Program
Templeton World Charity Foundation, Inc.UNSPECIFIED
Subject Keywords:gravitational waves; methods: data analysis
Other Numbering System:
Other Numbering System NameOther Numbering System ID
LIGO DocumentP1600181
PubMed Central ID:PMC5927389
Record Number:CaltechAUTHORS:20170410-100436817
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20170410-100436817
Official Citation:Bence Bécsy et al 2017 ApJ 839 15
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:76468
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:10 Apr 2017 18:19
Last Modified:04 May 2018 16:31

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