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

169 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

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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