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Data analysis strategies for the detection of gravitational waves in non-Gaussian noise

Creighton, Jolien D. E. (1999) Data analysis strategies for the detection of gravitational waves in non-Gaussian noise. Physical Review D, 60 (2). Art. No. 021101. ISSN 2470-0010. doi:10.1103/PhysRevD.60.021101.

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In order to analyze data produced by the kilometer-scale gravitational wave detectors that will begin operation early next century, one needs to develop robust statistical tools capable of extracting weak signals from the detector noise. This noise will likely have non-stationary and non-Gaussian components. To facilitate the construction of robust detection techniques, I present a simple two-component noise model that consists of a background of Gaussian noise as well as stochastic noise bursts. The optimal detection statistic obtained for such a noise model incorporates a natural veto which suppresses spurious events that would be caused by the noise bursts. When two detectors are present, I show that the optimal statistic for the non-Gaussian noise model can be approximated by a simple coincidence detection strategy. For simulated detector noise containing noise bursts, I compare the operating characteristics of (i) a locally optimal detection statistic (which has nearly optimal behavior for small signal amplitudes) for the non-Gaussian noise model, (ii) a standard coincidence-style detection strategy, and (iii) the optimal statistic for Gaussian noise.

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Additional Information:© 1999 American Physical Society. (Received 28 January 1999; published 14 June 1999) I would like to thank Bruce Allen, Patrick Brady, Éanna Flanagan, and Kip Thorne for their many useful comments on this paper. This work was supported by the National Science Foundation Grant No. PHY-9424337.
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Issue or Number:2
Classification Code:PACS number~s!: 04.80.Nn, 07.05.Kf
Record Number:CaltechAUTHORS:20170408-165546710
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:76282
Deposited By: 1Science Import
Deposited On:12 Mar 2018 20:30
Last Modified:15 Nov 2021 16:58

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