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A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
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We have developed a convolutional neural network (CNN) that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a $\nu_\mu$ charged current neutral pion data samples.
Forward citations
Cited by 2 Pith papers
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Enhanced Ionization Charge Identification in the Short-Baseline Neutrino Program Neutrino Detectors with Deep Neural Networks
A DNN-based region of interest detection method for SBN neutrino detectors outperforms traditional wire-by-wire thresholding in identification accuracy and reconstruction quality while being more robust to performance...
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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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