335 citations · 1.2k across the 81 of their papers we have counts for
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Hunting for new glitches in LIGO data using community science
E Mackenzie, C P L Berry, G Niklasch +5
Data from ground-based gravitational-wave detectors like LIGO contain many types of noise. Glitches are short bursts of non-Gaussian noise that may hinder our ability to identify o…
Advancing Glitch Classification in Gravity Spy: Multi-view Fusion with Attention-based Machine Learning for Advanced LIGO's Fourth Observing Run
Yunan Wu, Michael Zevin, Christopher P. L. Berry +7
The first successful detection of gravitational waves by ground-based observatories, such as the Laser Interferometer Gravitational-Wave Observatory (LIGO), marked a breakthrough i…
Gravity Spy: Lessons Learned and a Path Forward
Michael Zevin, Corey B. Jackson, Zoheyr Doctor +23
The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-scienc…
Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
S Soni, C P L Berry, S B Coughlin +18
The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and ide…
Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
Michael Zevin, Scott Coughlin, Sara Bahaadini +15
(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of as…