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

Journal Article · · Physical Review. D.
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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.

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Univ. of Michigan, Ann Arbor, MI (United States); Harvard Univ., Cambridge, MA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), Nuclear Physics (NP)
Contributing Organization:
MicroBooNE; MicroBooNE Collaboration
Grant/Contract Number:
AC02-07CH11359; SC0007859; SC0012704; AC05-76RL01830; SC0007881
OSTI ID:
1511498
Alternate ID(s):
OSTI ID: 1468407; OSTI ID: 1514380; OSTI ID: 1529985; OSTI ID: 1638589; OSTI ID: 1784068
Report Number(s):
FERMILAB-PUB-18-231-ND; arXiv:1808.07269; BNL-211676-2019-JAAM; PNNL-SA-137800; PRVDAQ; oai:inspirehep.net:1689384
Journal Information:
Physical Review. D., Vol. 99, Issue 9; ISSN 2470-0010
Publisher:
American Physical Society (APS)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 30 works
Citation information provided by
Web of Science

Figures / Tables (24)

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