Enhancing the significance of gravitational wave bursts through signal classification

Abstract

The quest to observe gravitational waves challenges our ability to discriminate signals from detector noise. This issue is especially relevant for transient gravitational waves searches with a robust eyes wide open approach, the so called all- sky burst searches. Here we show how signal classification methods inspired by broad astrophysical characteristics can be implemented in all-sky burst searches preserving their generality. In our case study, we apply a multivariate analyses based on artificial neural networks to classify waves emitted in compact binary coalescences. We enhance by orders of magnitude the significance of signals belonging to this broad astrophysical class against the noise background. Alternatively, at a given level of mis-classification of noise events, we can detect about 1/4 more of the total signal population. We also show that a more general strategy of signal classification can actually be performed, by testing the ability of artificial neural networks in discriminating different signal classes. The possible impact on future observations by the LIGO-Virgo network of detectors is discussed by analysing recoloured noise from previous LIGO-Virgo data with coherent WaveBurst, one of the flagship pipelines dedicated to all-sky searches for transient gravitational waves.

References in corpus (5)

• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• LIGO: The Laser Interferometer Gravitational-Wave Observatory
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• Multivariate Classification with Random Forests for Gravitational Wave Searches of Black Hole Binary Coalescence
• Quasi-normal modes of superfluid neutron stars

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• Some Optimizations on Detecting Gravitational Wave Using Convolutional Neural Network
• Optimization of model independent gravitational wave search using machine learning
• Search for gravitational-wave bursts in the third Advanced LIGO-Virgo run with coherent WaveBurst enhanced by Machine Learning
• 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