Enhancing the sensitivity of transient gravitational wave searches with Gaussian mixture models

Gayathri, V., Lopez, D., R. S., P., Heng, I. S. , Pai, A. and Messenger, C. (2020) Enhancing the sensitivity of transient gravitational wave searches with Gaussian mixture models. Physical Review D, 102(10), 104023. (doi: 10.1103/PhysRevD.102.104023)

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Identifying the presence of a gravitational wave transient buried in nonstationary, non-Gaussian noise, which can often contain spurious noise transients (glitches), is a very challenging task. For a given dataset, transient gravitational wave searches produce a corresponding list of triggers that indicate the possible presence of a gravitational wave signal. These triggers are often the result of glitches mimicking gravitational wave signal characteristics. To distinguish glitches from genuine gravitational wave signals, search algorithms estimate a range of trigger attributes, with thresholds applied to these trigger properties to separate signal from noise. Here, we present the use of Gaussian mixture models, a supervised machine learning approach, as a means of modeling the multidimensional trigger attribute space. We demonstrate this approach by applying it to triggers from the coherent Waveburst search for generic bursts in LIGO O1 data. By building Gaussian mixture models for the signal and background noise attribute spaces, we show that we can significantly improve the sensitivity of the coherent Waveburst search and strongly suppress the impact of glitches and background noise, without the use of multiple search bins as employed by the original O1 search. We show that the detection probability is enhanced by a factor of 10, leading enhanced statistical significance for gravitational wave signals such as GW150914.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Messenger, Dr Christopher and Heng, Professor Ik Siong
Authors: Gayathri, V., Lopez, D., R. S., P., Heng, I. S., Pai, A., and Messenger, C.
College/School:College of Science and Engineering > School of Physics and Astronomy
Journal Name:Physical Review D
Publisher:American Physical Society
ISSN (Online):2470-0029
Published Online:09 November 2020
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300569Strengthening capacity in big data and engineering through LIGO-IndiaGiles HammondScience and Technology Facilities Council (STFC)ST/R001928/1P&S - Physics & Astronomy