WG1Machine Learning for Gravitational Wave astronomy
Led by Michal Bejger (INFN and CAMK) and Annalisa Appice (UniBa).
This working group aims at investigating Machine Learning (ML) techniques to classify Gravitational Wave (GW) signals, recognise noise and disturbances from the instrument, as well as identify GW signals from known mechanisms and GW signals from yet unknown mechanisms...
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WG2Machine Learning for low-frequency seismic measurement
Led by Dr Velimir Ilić (MISANU, Serbia) and Fabio Bonsignorio (Heron Robots, Italy)
The performance of Earth-based GW detectors is largely influenced by the ability of combating the low-frequency ambient seismic noise and other seismic influences. These tasks require multidisciplinary research in the fields of seismic sensing, signal processing, robotics, machine learning and mathematical modeling...
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WG3Machine Learning for Advanced Control techniques
Led Dr Luigia Petre (Åbo Akademi University, Turku, Finland) and Francesca Badaracco (Gran Sasso Science Institute, L'Aquila, 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....
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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.
