30 August – 2 September 2021
WG3 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.
In this training school we address the following topics:
· Einstein Telescope
· LISA
· Deep learning taxonomy
· Newtonian noise cancellation with machine learning
· Fractal analysis for controlling interferometers
· Robotics for interferometers
The school will take place online from August 30 to September 2, 2021, and it is organised by the group in Turku (Finland).
More info can be found at the web page of the training school, including the list of speakers who have kindly agreed to join us: https://indico.ego-gw.it/event/217/
Registration is still open until 29 August 2021 at:
https://indico.ego-gw.it/event/217/registrations/282/
The recordings of the lectures can be found at:
https://indico.ego-gw.it/event/217/contributions/
