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

(MicroBooNE Collaboration)

Research output: Contribution to journalArticle

1 Citation (Scopus)

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 (US)
Article number092001
JournalPhysical Review D
Volume99
Issue number9
DOIs
StatePublished - May 1 2019

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chambers
projection
pixels
argon
electromagnetism
liquids
software development tools
neutral currents
stopping
muons
pions
education
detectors
predictions

ASJC Scopus subject areas

  • Physics and Astronomy (miscellaneous)

Cite this

Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber. / (MicroBooNE Collaboration).

In: Physical Review D, Vol. 99, No. 9, 092001, 01.05.2019.

Research output: Contribution to journalArticle

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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.",
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