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.

Original languageEnglish
Article number092001
JournalPhysical Review D
Volume99
Issue number9
DOIs
StatePublished - 1 May 2019

Funding

FundersFunder number
Not addedST/N000447/1, ST/R00014X/1
Fermi Research Alliance, LLCDE-AC02-07CH11359
High Energy Physics and Nuclear Physics
Science and Technology Facilities Council of the United Kingdom
National Science Foundation1801996
U.S. Department of Energy
Office of Science
Science and Technology Facilities CouncilST/R000271/1
Royal Society
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

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