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.
@article{arxiv.2106.09911,
title = {Extracting low energy signals from raw LArTPC waveforms using deep learning techniques -- A proof of concept},
author = {Lorenzo Uboldi and David Ruth and Michael Andrews and Michael H. L. S. Wang and Hans-Joachim Wenzel and Wanwei Wu and Tingjun Yang},
journal= {arXiv preprint arXiv:2106.09911},
year = {2022}
}