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 the training school we address the following topics:
The workshop is organised during August 30-September 2, 2021 in Turku, Finland or online. The format of the school is to be decided in the middle of July. We encourage you to register and indicate whether you'd be comfortable travelling for the school or not.
The following speakers have kindly agreed to join us:
These speakers will also oversee a round table discussion on Thursday, September 2, mainly on investigating the machine learning potential in their topics, but also discussing other topics of interest.