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A Convolutional Neural Network Neutrino Event Classifier

Journal Article · · JINST
 [1];  [2];  [3];  [4];  [5];  [4];  [3];  [5];  [1];  [2]
  1. Cincinnati U.
  2. William-Mary Coll.
  3. Minnesota U.
  4. Fermilab
  5. Indiana U.
+ Show Author Affiliations
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.
Research Organization:
Cincinnati U.; Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Indiana U.; Minnesota U.; William-Mary Coll.
Sponsoring Organization:
US Department of Energy
Grant/Contract Number:
AC02-07CH11359
OSTI ID:
1322151
Alternate ID(s):
OSTI ID: 22664964
Report Number(s):
FERMILAB-PUB-16-082-ND; oai:inspirehep.net:1444342; arXiv:1604.01444
Journal Information:
JINST, Journal Name: JINST Journal Issue: 09 Vol. 11
Country of Publication:
United States
Language:
English

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Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
Neutrino detectors
calibration and fitting methods
cluster finding
particle identification methods
particle tracking detectors
pattern recognition