Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber
Name
PhysRevD.103.092003.pdf
Description
Published version
Size
13.75 MB
Format
Adobe PDF
Checksum (MD5)
7f6b67b9a3425f8c22c6048f5bb987f3
Author(s)
Conrad, Janet
Date Issued
2021
Journal
Physical Review D
Publisher
American Physical Society (APS)
Citation
Conrad, Janet. 2021. "Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber." Physical Review D, 103 (9).
Version
Final published version
Abstract
We present the multiple particle identification (MPID) network, a
convolutional neural network (CNN) for multiple object classification,
developed by MicroBooNE. MPID provides the probabilities of $e^-$, $\gamma$,
$\mu^-$, $\pi^\pm$, and protons in a single liquid argon time projection
chamber (LArTPC) readout plane. The network extends the single particle
identification network previously developed by MicroBooNE. MPID takes as input
an image either cropped around a reconstructed interaction vertex or containing
only activity connected to a reconstructed vertex, therefore relieving the tool
from inefficiencies in vertex finding and particle clustering. The network
serves as an important component in MicroBooNE's deep learning based $\nu_e$
search analysis. In this paper, we present the network's design, training, and
performance on simulation and data from the MicroBooNE detector.
MIT Department
Massachusetts Institute of Technology. Department of Physics
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Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1103/PHYSREVD.103.092003
