A convolutional neural network neutrino event classifier

A. Aurisano, A. Radovic, D. Rocco, A. Himmel, M. D. Messier, E. Niner, G. Pawloski, F. Psihas, A. Sousa, P. Vahle

Research output: Contribution to journalArticlepeer-review

137 Scopus citations


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.

Original languageEnglish (US)
Article numberP09001
JournalJournal of Instrumentation
Issue number9
StatePublished - Sep 1 2016

Bibliographical note

Funding Information:
The authors thank the NOvA collaboration for use of its Monte Carlo simulation software and related tools. We thank Gustav Larsson for useful conversations, and we are grateful for Amitoj Singh and the Fermilab Scientific Computing Division's efforts installing Caffe and maintaining the GPU cluster used for training. This work was supported by the US Department of Energy and the US National Science Foundation. NOvA receives additional support from the Department of Science and Technology, India; the European Research Council; the MSMT CR, Czech Republic; the RAS, RMES, and RFBR, Russia; CNPq and FAPEG, Brazil; and the State and University of Minnesota. We are grateful for the contributions of the staff at the Ash River Laboratory, Argonne National Laboratory, and Fermilab. Fermilab is operated by Fermi Research Alliance, LLC under Contract No. De- AC02-07CH11359 with the US DOE.

Publisher Copyright:
© 2016 IOP Publishing Ltd and Sissa Medialab srl.


  • Neutrino detectors
  • Particle identification methods
  • Particle tracking detectors
  • Pattern recognition, cluster finding, calibration and fitting methods


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