  The liquid argon time projection chamber (LArTPC) detector technology has an
excellent capability to measure properties of low-energy neutrinos produced by
the sun and supernovae and to look for exotic physics at very low energies. In
order to achieve those physics goals, it is crucial to identify and reconstruct
signals in the waveforms recorded on each TPC wire. In this paper, we report on
a novel algorithm based on a one-dimensional convolutional neural network (CNN)
to look for the region-of-interest (ROI) in raw waveforms. We test this
algorithm using data from the ArgoNeuT experiment in conjunction with an
improved noise mitigation procedure and a more realistic data-driven noise
model for simulated events. This deep-learning ROI finder shows promising
performance in extracting small signals and gives an efficiency approximately
twice that of the traditional algorithm in the low energy region of
$\sim$0.03-0.1 MeV. This method offers great potential to explore low-energy
physics using LArTPCs.
