Wu, Y., Zevin, M., Berry, C., Crowston, K., Østerlund, C., Doctor, Z., Banagiri, S., Jackson, C., Kalogera, V., & Katsaggelos, A. (2025). Advancing Glitch Classification in Gravity Spy: Multi-view Fusion with Attention-based Machine Learning for Advanced LIGO’s Fourth Observing Run. Classical and Quantum Gravity. https://doi.org/10.1088/1361-6382/adf58b
Abstract

The first successful detection of gravitational waves by ground-based observatories, such as the Laser Interferometer Gravitational-Wave Observatory (LIGO), marked a breakthrough in our comprehension of the Universe. However, due to the unprecedented sensitivity required to make such observations, gravitational-wave detectors also capture disruptive noise sources called glitches, which can potentially be confused for or mask gravitational-wave signals.

Year of Publication
2025
Journal
Classical and Quantum Gravity
DOI
10.1088/1361-6382/adf58b