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We present an enhanced method for the application of Gaussian mixture modeling (GMM) to the coherent WaveBurst (cWB) algorithm in the search for short-duration gravitational wave (GW) transients. The supervised machine learning method of GMM allows for the multidimensional distributions of noise and signal to be modeled over a set of representative attributes, which aids in the classification of GW signals against noise transients (glitches) in the data. We demonstrate that updating the approach to model construction eliminates bias previously seen in the GMM analysis, increasing the robustness and sensitivity of the analysis over a wider range of burst source populations. The enhanced methodology is applied to the generic burst all-sky short search in the LIGO-Virgo full third observing run (O3), marking the first application of GMM to the 3 detector Livingston-Hanford-Virgo network. For both 2- and 3- detector networks, we observe comparable sensitivities to an array of generic signal morphologies, with significant sensitivity improvements to waveforms in the low quality factor parameter space at false alarm rates of 1 per 100 years. This proves that GMM can effectively mitigate blip glitches, which are one of the most problematic sources of noise for unmodeled GW searches. The cWB-GMM search recovers similar numbers of compact binary coalescence (CBC) events as other cWB postproduction methods, and concludes on no new gravitational wave detection after known CBC events are removed. Published by the American Physical Society2024
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This content will become publicly available on March 14, 2027
Optimizing searches for gravitational wave bursts using coherent WaveBurst-2G
Abstract The most general searches for gravitational wave transients (GWTs) rely on data analysis methods that do not assume prior knowledge of the signal’s waveform, direction, and arrival time on Earth. These searches provide data-driven signal reconstructions that are crucial both for testing available GW emission models and for discovering yet-to-be-unveiled sources. Here, we discuss the progresses in detection performances of the coherent WaveBurst second generation pipeline (cWB-2G), which is highly adaptable to minimally–modeled as well as model–informed searches for GWTs. Several search configurations for GWTs are examined using approximately 14.8 d of observation time from the third observing run by LIGO-Virgo-KAGRA (LVK). Recent enhancements include a ranking statistic fully based on multivariate classification with eXtreme gradient boosting, a thorough validation of the accuracy of the statistical significance of GWT candidates, and the measurement of the correlations of false alarms and simulated detections between different concurrent searches. For the first time, we provide a comprehensive comparison of cWB-2G performances on data from networks made of two and three detectors, and we demonstrate the advantage of combining concurrent searches for GWTs of generic morphology in a global observatory. This work offers essential insights for assessing our data analysis strategies in the ongoing and future LVK searches for generic GWTs.
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- Award ID(s):
- 2513294
- PAR ID:
- 10704874
- Publisher / Repository:
- Institute of Physics
- Date Published:
- Journal Name:
- Classical and Quantum Gravity
- Volume:
- 43
- Issue:
- 5
- ISSN:
- 0264-9381
- Page Range / eLocation ID:
- 055016
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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