Exploiting the latent space of deep AutoEncoders for the identification of signal pulses in noisy time-series
We propose a data-driven procedure, based on convolutional variational autoencoders, to identify the presence of signal pulses in long time-series. The dataset consists of synthetic waveforms, each composed of non-gaussian noise and a log-normal shaped signal of variable intensity, with a length of 10,000 samples. The model heavily compresses the input waveforms, allowing a direct study of such a reduced representation. After training for 150 epochs on 7,500 waveforms, a region in the latent space where the network encodes time-series presenting only background noise emerges, allowing in turn to tag as candidates for containing a signal those falling outside. When applied on a test dataset of freshly generated waveforms, 100% of the events with a large pulses are correctly labelled, and this fraction only decreases for signal amplitudes comparable with accidental noise pulses. This approach was designed to fully exploit the measurements in dual-phase Liquid Argon Time Projection Chambers, as the one of the Recoil Directionality experiment, built in the context of the Darkside project.