Identification of noise artifacts in searches for long-duration gravitational-wave transients

Tanner Prestegard, Eric Thrane, Nelson L. Christensen, Michael W. Coughlin, Ben Hubbert, Shivaraj Kandhasamy, Evan MacAyeal, Vuk Mandic

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

We present an algorithm for the identification of transient noise artifacts (glitches) in cross-correlation searches for long gravitational-wave (GW) transients lasting seconds to weeks. The algorithm utilizes the auto-power in each detector as a discriminator between well-behaved stationary noise (possibly including a GW signal) and non-stationary noise transients. We test the algorithm with both Monte Carlo noise and time-shifted data from the LIGO S5 science run and find that it removes a significant fraction of glitches while keeping the vast majority (99.6%) of the data. We show that this cleaned data can be used to observe GW signals at a significantly lower amplitude than can otherwise be achieved. Using an accretion disk instability signal model, we estimate that the algorithm is accidentally triggered at a rate of less than 10 5% by realistic signals, and less than 3% even for exceptionally loud signals. We conclude that the algorithm is a safe and effective method for cleaning the cross-correlation data used in searches for long GW transients.

Original languageEnglish (US)
Article number095018
JournalClassical and Quantum Gravity
Volume29
Issue number9
DOIs
StatePublished - May 7 2012

Bibliographical note

Funding Information:
J.A.M. acknowledges the Belgian N.F.S.R. for a position as Research Associate. This research has been sponsored by the Belgian government within the frame of an IUAP Center of Excellence programme.

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