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WORKING GROUPS

WG_1

WG1
Machine Learning for Gravitational Wave astronomy

Led by Michal Bejger (APC 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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seismic

WG2
Machine 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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wg_3

WG3
Machine Learning for Advanced Control techniques

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....
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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.