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Title: A convolutional neural network neutrino event classifier

Abstract

Here, convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

Authors:
 [1];  [2];  [3];  [4];  [5];  [4];  [3];  [5];  [1];  [2]
  1. Univ. of Cincinnati, Cincinnati, OH (United States)
  2. College of William and Mary, Williamsburg, VA (United States)
  3. Univ. of Minnesota, Minneapolis, MN (United States)
  4. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
  5. Indiana Univ., Bloomington, IN (United States)
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1322151
Report Number(s):
FERMILAB-PUB-16-082-ND; arXiv:1604.01444
Journal ID: ISSN 1748-0221; 1444342
Grant/Contract Number:
AC02-07CH11359
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Instrumentation
Additional Journal Information:
Journal Volume: 11; Journal Issue: 09; Journal ID: ISSN 1748-0221
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; particle identification methods; pattern recognition; cluster finding; calibration and fitting methods; Neutrino detectors; particle tracking detectors

Citation Formats

Aurisano, A., Radovic, A., Rocco, D., Himmel, A., Messier, M. D., Niner, E., Pawloski, G., Psihas, F., Sousa, A., and Vahle, P. A convolutional neural network neutrino event classifier. United States: N. p., 2016. Web. doi:10.1088/1748-0221/11/09/P09001.
Aurisano, A., Radovic, A., Rocco, D., Himmel, A., Messier, M. D., Niner, E., Pawloski, G., Psihas, F., Sousa, A., & Vahle, P. A convolutional neural network neutrino event classifier. United States. doi:10.1088/1748-0221/11/09/P09001.
Aurisano, A., Radovic, A., Rocco, D., Himmel, A., Messier, M. D., Niner, E., Pawloski, G., Psihas, F., Sousa, A., and Vahle, P. Thu . "A convolutional neural network neutrino event classifier". United States. doi:10.1088/1748-0221/11/09/P09001. https://www.osti.gov/servlets/purl/1322151.
@article{osti_1322151,
title = {A convolutional neural network neutrino event classifier},
author = {Aurisano, A. and Radovic, A. and Rocco, D. and Himmel, A. and Messier, M. D. and Niner, E. and Pawloski, G. and Psihas, F. and Sousa, A. and Vahle, P.},
abstractNote = {Here, convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.},
doi = {10.1088/1748-0221/11/09/P09001},
journal = {Journal of Instrumentation},
number = 09,
volume = 11,
place = {United States},
year = {Thu Sep 01 00:00:00 EDT 2016},
month = {Thu Sep 01 00:00:00 EDT 2016}
}

Journal Article:
Free Publicly Available Full Text
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Cited by: 11works
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