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Transient broadband noise in gravitational wave (GW) detectors-also known as noise triggers (referred to as triggers for brevity)-can often be a deterrant to the efficiency with which astrophysical search pipelines detect sources. It is important to understand their instrumental or environmental origin so that they could be eliminated or accounted for in the data. Since the number of triggers is large, data mining approaches such as clustering and classification are useful tools for this task. Classification of triggers based on a handful of discrete properties has been done in the past. A rich information content is available in the waveform or \"shape\" of the triggers that has had a rather restricted exploration so far. This paper presents a new way to classify triggers deriving information from both trigger waveforms as well as their discrete physical properties, using a sequential combination of the longest common subsequence (LCSS) and LCSS coupled with Fast Time Series Evaluation (FTSE) for waveform classification, and the multidimensional hierarchical classification (MHC) analysis for the grouping based on physical properties. A generalized k-means algorithm is used with the LCSS (and LCSS+FTSE) for clustering the triggers using a validity measure to determine the correct number of clusters in absence of any prior knowledge. The results have been demonstrated by simulations and by application to a segment of real LIGO data from the sixth science run. © 2012 American Physical Society.


© Physical Review D - Particles, Fields, Gravitation and Cosmology. Original version available at:

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Physical Review D - Particles, Fields, Gravitation and Cosmology





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