Deep convolutional neural network to characterize transient noise in gravitational-wave detectors

Gravitational-wave detectors are affected by many noise sources, including transient events called glitches, originating from instrumental or environmental disturbances, that make the noise of a detector far from being stationary and gaussian. Glitches affect data quality, and can mimic astrophysical signals or even mask them. Therefore, it is fundamental to recognize these transients in order to cluster them in families of similar morphology and investigate their origin. In this paper we discuss the possibility of tackling this task with a deep convolutional neural network.