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Title: 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 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 νμ charged-current neutral pion data samples.

Authors:
;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Brookhaven National Lab. (BNL), Upton, NY (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25); USDOE Office of Science (SC), Nuclear Physics (NP) (SC-26)
Contributing Org.:
MicroBooNE; MicroBooNE Collaboration 1
OSTI Identifier:
1511498
Alternate Identifier(s):
OSTI ID: 1468407; OSTI ID: 1514380
Report Number(s):
arXiv:1808.07269; FERMILAB-PUB-18-231-ND; BNL-211676-2019-JAAM
1689384
Grant/Contract Number:  
AC02-07CH11359; SC0012704
Resource Type:
Published Article
Journal Name:
Phys.Rev.
Additional Journal Information:
Journal Volume: D99; Journal Issue: 9
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; neural; pixel; time; projection; chamber

Citation Formats

Adams, C., and et al.. Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber. United States: N. p., 2019. Web. doi:10.1103/PhysRevD.99.092001.
Adams, C., & et al.. Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber. United States. doi:10.1103/PhysRevD.99.092001.
Adams, C., and et al.. Wed . "Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber". United States. doi:10.1103/PhysRevD.99.092001.
@article{osti_1511498,
title = {Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber},
author = {Adams, C. and et al.},
abstractNote = {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 νμ charged-current neutral pion data samples.},
doi = {10.1103/PhysRevD.99.092001},
journal = {Phys.Rev.},
number = 9,
volume = D99,
place = {United States},
year = {2019},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1103/PhysRevD.99.092001

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