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

Abstract

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.

15 pages, 12 figures

References in corpus (5)

• Neutrino interaction classification with a convolutional neural network in the DUNE far detector
• A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
• First Measurement of Electron Neutrino Scattering Cross Section on Argon
• The Continuous Readout Stream of the MicroBooNE Liquid Argon Time Projection Chamber for Detection of Supernova Burst Neutrinos
• Extracting low energy signals from raw LArTPC waveforms using deep learning techniques -- A proof of concept

Cited by in corpus (2)

• Extracting low energy signals from raw LArTPC waveforms using deep learning techniques -- A proof of concept
• Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing