335 citations · 502 across the 6 of their papers we have counts for
6 papers
Project Archetypes: A Blessing and a Curse for AI Development
Mateusz Dolata, Kevin Crowston, Gerhard Schwabe
Software projects rely on what we call project archetypes, i.e., pre-existing mental images of how projects work. They guide distribution of responsibilities, planning, or expectat…
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…
Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications
J. Glanzer, S. Banagiri, S. B. Coughlin +10
Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features tha…
Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research
Lea A. Shanley, Lucy Fortson, Tanya Berger-Wolf +2
Machine learning, artificial intelligence, and deep learning have advanced significantly over the past decade. Nonetheless, humans possess unique abilities such as creativity, intu…
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…