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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 Ilic and Allesandro Bertolini

This working group will review data available from past surveys of the Virgo detector site, made with arrays of seismic sensors, as well as current measurement and model extraction methods...
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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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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.