Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber

2018/08/22 by MicroBooNE collaboration, C. Adams, M. Alrashed +188 · 1 voice · 1 citation
Computer Science · Medicine · Physics and Astronomy · #Atomic and Subatomic Physics Research #Nuclear Physics and Applications #Radiation Therapy and Dosimetry #cs.CV #hep-ex #physics.data-an #physics.ins-det

paper · pdf · doi:10.1103/physrevd.99.092001

arxiv published 2018/08/22 · arxiv updated 2018/08/22 · openalex created_date 2018/08/31 · openalex publication_date 2019/05/07 · openalex updated_date 2026/07/31

Abstract

We have developed a convolutional neural network 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 \ensuremathν_\ensuremathμ charged-current neutral pion data samples.

Citations

• A Proposal for a Three Detector Short-Baseline Neutrino Oscillation Program in the Fermilab Booster Neutrino Beam
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Caffe: Convolutional Architecture for Fast Feature Embedding

Cited by

• Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors

Discussions

• A deep neural network for particle identification in a neutrino detector [hn, 3 points, 0 comments]

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