Malvasia – MAchine Learning to VAlue Single Interferometer Analysis

Project Summary

The direct observation of gravitational waves (GW) by the LIGO and Virgo detectors is one of the breakthrough discoveries of the beginning of the 21st century. However, the searches for GW transient sources are mainly limited by non-Gaussian transient noise artefacts coming from a wide variety of provenances. Statistical modelling of these “instrumental glitches” has not been feasible, so far, because they vary widely in rate, duration, frequency range and morphology. Their contamination can be partially mitigated by requesting temporal coincidence in two or more detectors as their accidental co-occurrence probability is low. When only one detector is operating this strategy cannot be used. The aim of this project is to use deep learning algorithms to separate the glitches from the astrophysical signal, focusing in particular to periods when only one detector is taking data.

 Agata Trovato