The LIGO-Virgo-KAGRA network has now started the second half of its fourth observing run, called O4b. The first half of the fourth observing run, O4a, ran from May 2023 - January 2024 and already accumulated 81 new significant gravitational-wave candidates. One of these was a neutron star merging with a mass-gap object, which you can read more about here. Many more gravitational-wave events (and many more glitches) to come!
The LIGO-Virgo-KAGRA network has now started the second half of its fourth observing run, called O4b. The first half of the fourth observing run, O4a, ran from May 2023 - January 2024 and already accumulated 81 new significant gravitational-wave candidates. One of these was a neutron star merging with a mass-gap object, which you can read more about here. Many more gravitational-wave events (and many more glitches) to come!
Results
Since its launch in 2016, the Gravity Spy project has led to multiple publications, and the identification of several new glitch classes. You can read about our results in the Gravity Spy Blog.
In the beta testing for the Gravity Spy, the project engaged over 1,400 registered volunteers who delivered over 45,000 glitch classifications! These classifications were incredibly useful in building larger sets of morphologically-similar glitches to be analyzed by LIGO detector characterization experts, and have helped to train better machine learning classification algorithms.
In addition, during beta testing citizen scientists uncovered two new morphologies of glitches, the 'Paired Doves' glitch (below left) and the 'Helix' glitch (below right). In particular, the chirpy nature of the paired doves glitch afflicted searches for gravitational waves in the data, and its discovery in Gravity Spy led to LIGO scientists discovering its cause.
These options are now available for classification in the advanced workflows. To learn more about these glitches and their potential causes, check out the field guide!
During the third observing run (O3) of Advanced LIGO, volunteers identified further new candidates. Of particular note was the Discovery of the 'Crown' class, which was separately identified by LIGO detector characterisation experts, who named it 'Fast Scattering'. By comparing the 'Crown' and 'Fast Scatter' classes, and using them to train our machine learning algorithm, we were able to show not only that Gravity Spy volunteers can identify new glitch classes, but their results can be comparable to those from experts! These findings are published in Soni et al. (2021), with several Gravity Spy volunteers included as coauthors.
Published and Submitted Gravity Spy Articles and Conference Proceedings
M. Zevin, S. Coughlin, S. Bahaadini, E. Besler, N. Rohani, S. Allen, M. Cabero, K. Crowston, A. K. Katsaggelos, S. L. Larson, T. K. Lee, C. Lintott, T. B. Littenberg, A. Lundgren, C. Østerlund, J. R. Smith, L. Trouille, & V. Kalogera. (2017). Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science, Classical and Quantum Gravity, 34, 064003.
S. Bahaadini, N. Rohani, S. Coughlin, M. Zevin, V. Kalogera, & A. Katsaggelos. (2017). Deep multi-view models for glitch classification, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), New Orleans, LA.
K. Crowston, & The Gravity Spy Collaboration. (2017). Gravity Spy: Humans, machines and the future of citizen science, ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2017). Portland, OR.
K. Crowston, C. Østerlund, T. Kyoung Lee. (2017). Blending machine and human learning processes, Hawai'i International Conference on System Sciences.
T. Kyoung Lee, K. Crowston, C. Østerlund, & G. Miller. (2017). Recruiting messages matter: Message strategies to attract citizen scientists, ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2017). Portland, OR.
S. Bahaadini, V. Noroozi, N. Rohani, S. Coughlin, M. Zevin, J. R. Smith, V. Kalogera, & A. Katsaggelos. (2018). Machine learning for Gravity Spy: Glitch classification and dataset, Information Sciences Journal, 444, 172–186.
C.B. Jackson, K. Crowston, C. Østerlund, & M. Harandi (2018). Folksonomies to support coordination and coordination of folksonomies, Computer Supported Cooperative Work, 27(3–6), 647–678.
T. Kyoung Lee, K. Crowston, M. Harandi, C. Østerlund, & G. Miller (2018). Appealing to different motivations in a message to recruit citizen scientists: results of a field experiment., Journal of Science Communication, 17.
C.B. Jackson, K. Crowston, & C. Østerlund (2018). Did they login? Patterns of anonymous contributions to online communities., Proceedings of the ACM on Human-Computer Interaction, 1(CSCW). Presented at the ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2018), Jersey City, NJ, 4–7 November.
S. Bahaadini, V. Noroozi, N. Rohani, S. Coughlin, M. Zevin, & A. Katsaggelos (2018). DIRECT: Deep DIscRiminative Embedding for ClusTering of LIGO Data.. IEEE International Conference on Image Processing.
S. Coughlin, S. Bahaadini, N. Rohani, M. Zevin, O. Patane, M. Harandi, C. Jackson, V. Noroozi, S. Allen, J. Areeda, M. Coughlin, P. Ruiz, C. P. L. Berry, K. Crowston, A. K. Katsaggelos, A. Lundgren, C. Østerlund, J. R. Smith, L. Trouille, V. Kalogera (2019). Classifying the unknown: Discovering novel gravitational-wave detector glitches using similarity learning. Physical Review D, 99, 082002.
C. Jackson, C. Østerlund, K. Crowston, M. Harandi, S. Allen, S. Bahaadini, S. Coughlin, V. Kalogera, A. Katsaggelos, S. Larson, N. Rohani, J. Smith, L. Trouille, & M. Zevin (2020). Teaching citizen scientists to categorize glitches using machine learning guided training, Computers in Human Behavior, 105, 106198.
C. Jackson, C. Østerlund, K. Crowston, M. Harandi, & L. Trouille (2020). Shifting Forms of Engagement: Volunteer Learning in Online Citizen Science. Proceedings of the ACM on Human-Computer Interaction, 4 (CSCW1), 1-19.
S. Soni, C. Berry, S. Coughlin, M. Harandi, C. Jackson, K. Crowston, C. Østerlund, O. Patane, A. Katsaggelos, L. Trouille, V. Baranowski, W. Domainko, K. Kaminski, M. Rodriguez, U. Marciniak, P. Nauta, G. Niklasch, R. Rote, B. Téglás, C. Unsworth, C. Zhang (2021). Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning, Classical and Quantum Gravity, 38, 195016.
J. Glanzer, S. Banagiri, S. B. Coughlin, S. Soni, M. Zevin, C. P. L. Berry, O. Patane, S. Bahaadini, N. Rohani, K. Crowston, V. Kalogera, C. Østerlund, A. Katsaggelos (2023). Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications, Classical and Quantum Gravity, 40, 065004.
M. Zevin, C. B. Jackson, Z. Doctor, Y. Wu, C. Østerlund, L. C. Johnson, C. P. L. Berry, K. Crowston, S. B. Coughlin, V. Kalogera, S. Banagiri, D. Davis, J. Glanzer, R. Hao, A. K. Katsaggelos, O. Patane, J. Sanchez, J. Smith, S. Soni, L. Trouille, M. Walker, I. Aerith, W. Domainko, V.-G. Baranowski, G. Niklasch, B. Téglás (2023). Gravity Spy: Lessons Learned and a Path Forward, European Physical Journal+ (accepted).
Y. Wu, M. Zevin, C. P. L. Berry, K. Crowston, C. Østerlund, Z. Doctor, S. Banagiri, C. B. Jackson, V. Kalogera, A. Katsaggelos (2024). Advancing Glitch Classification in Gravity Spy: Multi-view Fusion with Attention-based Machine Learning for Advanced LIGO's Fourth Observing Run, Information Sciences (submitted).