A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber

Abstract

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.

References in corpus (16)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Road Extraction by Deep Residual U-Net
• The GENIE Neutrino Monte Carlo Generator
• Design and Construction of the MicroBooNE Detector
• Unexplained Excess of Electron-Like Events From a 1-GeV Neutrino Beam
• The Neutrino Flux prediction at MiniBooNE
• A Proposal for a Three Detector Short-Baseline Neutrino Oscillation Program in the Fermilab Booster Neutrino Beam
• A Convolutional Neural Network Neutrino Event Classifier
• Ionization Electron Signal Processing in Single Phase LArTPCs II. Data/Simulation Comparison and Performance in MicroBooNE
• Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber
• Noise Characterization and Filtering in the MicroBooNE Liquid Argon TPC
• Ionization Electron Signal Processing in Single Phase LArTPCs I. Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation
• Background rejection in NEXT using deep neural networks
• Michel Electron Reconstruction Using Cosmic-Ray Data from the MicroBooNE LArTPC
• Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

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