Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
Name
1611.05531.pdf
Size
6.9 MB
Format
Adobe PDF
Checksum (MD5)
0875986f6aa34584dae963a7489bc91d
Author(s)
Bugel, Leonard G.
Conrad, Janet Marie
Hen, Or
Jones, Benjamin James Poyner
Wongjirad, Taritree
Date Issued
March 2017
Journal
Journal of Instrumentation
Publisher
IOP Publishing
Citation
Acciarri, R. et al. “Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber.” Journal of Instrumentation 12, 3 (March 2017): P03011–P03011 © 2017 IOP Publishing Ltd and Sissa Medialab
Version
Original manuscript
Abstract
We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Laboratory for Nuclear Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1088/1748-0221/12/03/P03011
