Speaker
Description
In this contribution, we propose a data-driven approach based on unsupervised learning, specifically using deep Convolutional AutoEncoders (CAE), to identify signals from low-energy nuclear recoils in the dual-phase Liquid Argon Time Projection Chamber (LAr TPC) of the Recoil Directionality (ReD) experiment. This is a challenging task for recoil energies $\sim$1 keV, since scintillation remains below detection thresholds and only a few ionization electrons are produced, resulting in a faint electroluminescence pulse (S2).
The dataset for training, validation, and testing includes $\sim$7,000 gamma-ray events from a $^{252}$Cf source, each corresponding to a 10,000-sample waveform averaged over 22 Silicon photomultiplier channels.
We trained four distinct CAE models, which heavily compress each input waveform into a latent space of dimensions 1 to 4, respectively. The aim is to directly study the features of such reduced representations, while comparing different compression levels. Indeed, after the training, a region encoding waveforms with negligible signal clearly emerges in every latent space, while larger pulses are encoded further away.
To calibrate and exploit this mapping as a signal identification method, $\sim$9,900 noise-only waveforms (from runs with the source shielded) are processed, defining the boundaries of pure background regions in the latent spaces; events falling outside are then tagged as signal candidates. The performance is evaluated across the four models on a "golden" neutron dataset of $\sim$600 waveforms, demonstrating that all the events selected in the standard analyses are correctly labelled when the CAE model has at least a 2-dimensional latent space.
Finally, we present a preliminary evaluation of the signal acceptance for our method, defined as the fraction of correctly tagged signals as a function of their integral, employing a dataset of semi-synthetic waveforms built by overlaying real pedestals with pure log-normal pulses, resembling S2 signals. The results are comparable to conventional strategies, offering a robust alternative for low-energy signal identification.