Led Dr Luigia Petre (Åbo Akademi University, Turku, Finland) and Dr Andrea Chincarini (Istituto Nazionale di Fisica Nucleare, Genova, Italy)
This working group explores the use of Machine Learning (ML) techniques in the control and noise mitigation strategies of scientific experiments, specifically for Gravitational Wave (GW) detectors. GW detectors, both those currently running and those foreseen to be spaceborne, are uniquely complex instruments with specific and new challenges in terms of control and noise issues. These challenges call for significant adaptation and ingenuity in the ML approaches, which are seldom used as textbook cases and are often coupled with simulations and burden with heavy experimental constraints. These developments need diverse expertise and interaction, which is the benefit of the current COST action. This working group’s goal is to develop ML algorithms as part of the detectors’ feedback-control systems as well as for the feed-forward cancellation of noise.
WG3 IS STRUCTURED IN SEVERAL TASKS IN THE FOLLOWING WAY:
1. ML for glitch removal
2. Newtonian noise cancellation with ML
3. Data preprocessing with reinforcement learning
4. Deep learning for noise removal
5. Laser cavity control to optimise locking time and stability
More documents (working plan, meeting notes, some slides) can be found in our slack channel: https://app.slack.com/client/TT226125S/CT51XP57Y.
WG3 meets on Zoom on the last Friday of every month, at 14:00 CET.
Get in touch if you are interested to join:
Dr. Eng. Mateusz Bawaj
Dr. Valerio Boschi
Prof. Peter Butka
Dr. Andrea Chincarini
Dr. Jan Harms
Dr. Natalia Korsakova
Dr. Luca Longo
Dr. Nikhil Mukund
Dr. Conor Muldoon
Franco Maria Nardini
Dr. Eng. Andrea Paoli
Dr. Luigia Petre
Dr. Maria Tringali
Dr. Catia Trubiani
Dr. Gabriele Vajente
Dr. Catalin Leordeanu
JOIN THE ACTION
The inclusion of further partners from currently participating COST Countries, or other countries within or outside the COST Network, is welcome and strongly encouraged during the entire duration of the Action.