Enhancing the sensitivity of transient gravitational wave searches with Gaussian Mixture Models

Abstract

Identifying the presence of a gravitational wave transient buried in non-stationary, non-Gaussian noise which can often contain spurious noise transients (glitches) is a very challenging task. For a given data set, 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 modelling the multi-dimensional 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.

9 pages, 4 figures

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• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Advanced LIGO
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• GW170608: Observation of a 19-solar-mass Binary Black Hole Coalescence
• GW190412: Observation of a Binary-Black-Hole Coalescence with Asymmetric Masses
• Coherent method for detection of gravitational wave bursts
• Machine-learning non-stationary noise out of gravitational wave detectors
• All-sky search for short gravitational-wave bursts in the first Advanced LIGO run
• Utilizing aLIGO Glitch Classifications to Validate Gravitational-Wave Candidates

Cited by in corpus (10)

• GW231123: a Binary Black Hole Merger with Total Mass 190-265 $M_{\odot }$
• Optimization of model independent gravitational wave search using machine learning
• An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients
• Utilizing Gaussian mixture models in all-sky searches for short-duration gravitational wave bursts
• Search for binary black hole mergers in the third observing run of Advanced LIGO-Virgo using coherent WaveBurst enhanced with machine learning
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Generalised gravitational burst generation with Generative Adversarial Networks
• Using supervised learning algorithms as a follow-up method in the search of gravitational waves from core-collapse supernovae
• Enhancing search pipelines for short gravitational wave transients with Gaussian mixture modelling
• Leveraging cross-detector parameter consistency measures to enhance sensitivities of gravitational wave searches