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

• Published in: Classical and quantum gravity 2024-04, Vol.41 (8), p.85007
• Main Authors: Álvarez-López, Sofía, Liyanage, Annudesh, Ding, Julian, Ng, Raymond, McIver, Jess
• Format: Article
• Language: English
• Subjects: glitch; gravitational-waves; Gravity Spy; LIGO-Virgo-KAGRA; machine learning; noise
• ISSN: 0264-9381; 1361-6382
• DOI: 10.1088/1361-6382/ad2194

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 compact binary coalescence (CBC) signal-vs-glitch classifier to accurately distinguish between glitches and GW signals. A CBC 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.