A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber

Understand

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.