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Improved time–frequency analysis of extreme-mass-ratio inspiral signals in mock LISA data

Gair, Jonathan R. and Mandel, Ilya and Wen, Linqing (2008) Improved time–frequency analysis of extreme-mass-ratio inspiral signals in mock LISA data. Classical and Quantum Gravity, 25 (18). Art. No. 184031. ISSN 0264-9381. http://resolver.caltech.edu/CaltechAUTHORS:GAIcqg08

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Abstract

The planned Laser Interferometer Space Antenna (LISA) is expected to detect gravitational wave signals from ~100 extreme-mass-ratio inspirals (EMRIs) of stellar-mass compact objects into massive black holes. The long duration and large parameter space of EMRI signals make data analysis for these signals a challenging problem. One approach to EMRI data analysis is to use time–frequency methods. This consists of two steps: (i) searching for tracks from EMRI sources in a time–frequency spectrogram and (ii) extracting parameter estimates from the tracks. In this paper we discuss the results of applying these techniques to the latest round of the Mock LISA Data Challenge, Round 1B. This analysis included three new techniques not used in previous analyses: (i) a new chirp-based algorithm for track search for track detection; (ii) estimation of the inclination of the source to the line of sight; (iii) a Metropolis–Hastings Monte Carlo over the parameter space in order to find the best fit to the tracks.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1088/0264-9381/25/18/184031DOIUNSPECIFIED
http://stacks.iop.org/CQG/25/184031PublisherUNSPECIFIED
http://www.iop.org/EJ/abstract/0264-9381/25/18/184031PublisherUNSPECIFIED
Additional Information:Copyright © Institute of Physics and IOP Publishing Limited 2008. Received 31 March 2008, in final form 29 June 2008. Published 2 September 2008. Print publication: Issue 18 (21 September 2008). JG thanks the Royal Society for support and the Albert Einstein Institute for hospitality and support while part of this work was being completed. IM was partially supported by NASA ATP Grant NNX07AH22G to Northwestern University. LW’s work is supported by the Alexander von Humboldt Foundation’s Sofja Kovalevskaja Programme funded by the German Federal Ministry of Education and Research. (Some figures in this article are in colour only in the electronic version)
Funders:
Funding AgencyGrant Number
Royal Society of LondonUNSPECIFIED
Albert Einstein InstituteUNSPECIFIED
NASANNX07AH22G
Alexander von Humboldt FoundationUNSPECIFIED
German Federal Ministry of Education and ResearchUNSPECIFIED
Issue or Number:18
Record Number:CaltechAUTHORS:GAIcqg08
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:GAIcqg08
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:11583
Collection:CaltechAUTHORS
Deposited By: Archive Administrator
Deposited On:08 Sep 2008 21:19
Last Modified:26 Dec 2012 10:16

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