@article{7601,
  author = {Michael Zevin and Corey Jackson and Zoheyr Doctor and Yunan Wu and Carsten Østerlund and Clifton Johnson and Christopher Berry and Kevin Crowston and Scott Coughlin and Vicky Kalogera and Sharan Banagiri and Derek Davis and Jane Glanzer and Renzhi Hao and Aggelos Katsaggelos and Oli Patane and Jennifer Sanchez and Joshua Smith and Siddharth Soni and Laura Trouille and Marissa Walker and Irina Aerith and Wilfried Domainko and Victor-Georges Baranowski and Gerhard Niklasch and Barbara Téglás},
  title = {Gravity Spy: Lessons Learned and a Path Forward},
  abstract = {<p>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-science volunteers and machine-learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine-learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine-learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.</p>},
  year = {2024},
  journal = {European Physical Journal Plus},
  volume = {139},
  pages = {Article 100},
  month = {01/2024},
  doi = {10.1140/epjp/s13360-023-04795-4},
  language = {eng},
}