Improved timefrequency analysis of extreme-mass-ratio inspiral signals in mock LISA data
Identifieur interne : 000191 ( Main/Exploration ); précédent : 000190; suivant : 000192Improved timefrequency analysis of extreme-mass-ratio inspiral signals in mock LISA data
Auteurs : Jonathan R. Gair [Royaume-Uni] ; Ilya Mandel [États-Unis, Inde] ; Linqing Wen [Allemagne, États-Unis, Australie]Source :
- Classical and Quantum Gravity [ 0264-9381 ] ; 2008.
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 timefrequency methods. This consists of two steps: (i) searching for tracks from EMRI sources in a timefrequency 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 MetropolisHastings Monte Carlo over the parameter space in order to find the best fit to the tracks.
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DOI: 10.1088/0264-9381/25/18/184031
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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 timefrequency methods. This consists of two steps: (i) searching for tracks from EMRI sources in a timefrequency 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. 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