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arXiv · 2304.09977

GSpyNetTree: A signal-vs-glitch classifier for gravitational-wave event candidates

Sofia Alvarez-Lopez·Annudesh Liyanage·Julian Ding·Raymond Ng·Jess McIver
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Abstract

Despite achieving sensitivities capable of detecting the extremely small amplitude of gravitational waves (GWs), LIGO and Virgo detector data contain frequent bursts of non-Gaussian transient noise, commonly known as 'glitches'. Glitches come in various time-frequency morphologies, and they are particularly challenging when they mimic the form of real GWs. Given the higher expected event rate in the next observing run (O4), LIGO-Virgo GW event candidate validation will require increased levels of automation. Gravity Spy, a machine learning tool that successfully classified common types of LIGO and Virgo glitches in previous observing runs, has the potential to be restructured as a signal-vs-glitch classifier to accurately distinguish between glitches and GW signals. A signal-vs-glitch classifier used for automation must be robust and compatible with a broad array of background noise, new sources of glitches, and the likely occurrence of overlapping glitches and GWs. We present GSpyNetTree, the Gravity Spy Convolutional Neural Network Decision Tree: a multi-CNN classifier using CNNs in a decision tree sorted via total GW candidate mass tested under these realistic O4-era scenarios.

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Sofia Alvarez-Lopez, Annudesh Liyanage, Julian Ding, Raymond Ng, Jess McIver. 2023-04-19. GSpyNetTree: A signal-vs-glitch classifier for gravitational-wave event candidates. https://arxiv.org/abs/2304.09977

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