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Machine Learning supervised classification to disentangle gravitational wave signals from noise events


The Virgo interferometer is the most sensitive gravitational-wave detector in Europe. It was realized in partnership between the French and Italian institutes CNRS and INFN. It involves a wide collaboration of almost 250 scientists and engineers. After a deep modification of the interferometer, to give birth to a new generation detector called Advanced Virgo (AdV), and after the successful science run together with the LIGO detectors, the instrument is now being commissioned to become fully operative for the second stage of sensitivity improvement. The EGO Consortium (European Gravitational Observatory), located in Cascina, near Pisa –Italy, hosts the Virgo detector. The detection of gravitational waves has inaugurated the era of gravitational astronomy and opened new avenues for the multimessenger study of cosmic sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo interferometers will probe a much larger volume of space and expand the capability of discovering new gravitational wave emitters. Noise of non-astrophysical origin contaminates science data taken by the Advanced Laser Interferometer Gravitational-wave Observatory and Advanced Virgo gravitational-wave detectors. Characterization of instrumental and environmental noise transients has proven critical in identifying false positives in the first observing runs. The introduction of Machine Learning advanced techniques can help in a fast identification and classification of different signals either for detector characterization goals or for signal detection one. We would test new data analysis pipelines to help the fast alert system and setup up and test new hardware and software prototype to reach these goals. The student will work on simulated data or open released data with the aim of tuning classification Machine Learning pipelines. ….  

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