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A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber
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A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber
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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.
Forward citations
Cited by 4 Pith papers
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Neutron Reconstruction via Blips in Liquid Argon Time Projection Chambers
Simulation-based proof-of-concept demonstrates neutron identification and reconstruction of direction and energy using blips from inelastic scattering in LArTPCs.
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Enhanced Ionization Charge Identification in the Short-Baseline Neutrino Program Neutrino Detectors with Deep Neural Networks
DNN ROI detection outperforms traditional wire-by-wire thresholding in identifying ionization signals in SBND and ICARUS detectors and shows greater robustness to performance variations.
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