  We investigate the feasibility of using deep learning techniques, in the form
of a one-dimensional convolutional neural network (1D-CNN), for the extraction
of signals from the raw waveforms produced by the individual channels of liquid
argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC
detector model is developed to generate realistic noise and signal waveforms
used to train and test the 1D-CNN, and evaluate its performance on low-level
signals. We demonstrate that our approach overcomes the inherent shortcomings
of traditional cut-based methods by extending sensitivity to signals with ADC
values below their imposed thresholds. This approach exhibits great promise in
enhancing the capabilities of future generation neutrino experiments like DUNE
to carry out their low-energy neutrino physics programs.
