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Rapid Bayesian position reconstruction for gravitational-wave transients

Singer, Leo P. and Price, Larry R. (2016) Rapid Bayesian position reconstruction for gravitational-wave transients. Physical Review D, 93 (2). Art. No. 024013. ISSN 2470-0010. http://resolver.caltech.edu/CaltechAUTHORS:20151103-084851270

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Abstract

Within the next few years, Advanced LIGO and Virgo should detect gravitational waves from binary neutron star and neutron star-black hole mergers. These sources are also predicted to power a broad array of electromagnetic transients. Because the electromagnetic signatures can be faint and fade rapidly, observing them hinges on rapidly inferring the sky location from the gravitational-wave observations. Markov chain Monte Carlo methods for gravitational-wave parameter estimation can take hours or more. We introduce BAYESTAR, a rapid, Bayesian, non-Markov chain Monte Carlo sky localization algorithm that takes just seconds to produce probability sky maps that are comparable in accuracy to the full analysis. Prompt localizations from BAYESTAR will make it possible to search electromagnetic counterparts of compact binary mergers.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1508.03634arXivDiscussion Paper
http://dx.doi.org/10.1103/PhysRevD.93.024013 DOIArticle
http://journals.aps.org/prd/abstract/10.1103/PhysRevD.93.024013PublisherArticle
ORCID:
AuthorORCID
Singer, Leo P.0000-0001-9898-5597
Alternate Title:WHOOMP! (There It Is) Rapid Bayesian position reconstruction for gravitational-wave transients
Additional Information:© 2016 American Physical Society. Received 28 September 2015; published 14 January 2016. We thank John Veitch and Will Farr for chairing a review of the analysis and code. We thank Britt Griswold for assistance with preparing Fig. 8. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the NSF and operates under Cooperative Agreement No. PHY-0107417. This research was supported by the NSF through a Graduate Research Fellowship to L. S. L. S. thanks the Aspen Center for Physics and NSF Grant No. 1066293 for hospitality during the editing of this paper. Source code for BAYESTAR is available as part of LALInference,17 the open source LIGO/Virgo parameter estimation toolchain, which is in turn part of LALSuite.18 This research made use of Astropy19 [73], a community-developed core Python package for astronomy. Some of the results in this paper have been derived using HEALPix20 [64]. Some results were produced on the NEMO computing cluster operated by the Center for Gravitation and Cosmology at University of Wisconsin–Milwaukee under NSF Grants No. PHY-0923409 and No. PHY-0600953.
Group:LIGO
Funders:
Funding AgencyGrant Number
NSFPHY-0107417
NSF Graduate Research FellowshipUNSPECIFIED
NSFPHY-1066293
NSFPHY-0923409
NSFPHY-0600953
Other Numbering System:
Other Numbering System NameOther Numbering System ID
LIGO DocumentLIGO-P1500009-v4
Issue or Number:2
Classification Code:PACS numbers: 04.80.Nn, 04.30.Tv, 02.50.Tt
Record Number:CaltechAUTHORS:20151103-084851270
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20151103-084851270
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:61788
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:03 Nov 2015 19:38
Last Modified:06 Sep 2019 22:13

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