Neutrino interaction classification with a convolutional neural network in the DUNE far detector Journal Articles uri icon

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abstract

  • The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure $CP$-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2-5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to $CP$-violating effects.

authors

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  • Weber, A
  • Weber, M
  • Wei, H
  • Weinstein, A
  • Wenman, D
  • Wetstein, M
  • While, MR
  • White, A
  • Whitehead, LH
  • Whittington, D
  • Wilking, MJ
  • Wilkinson, C
  • Williams, Z
  • Wilson, F
  • Wilson, RJ
  • Wolcott, J
  • Wongjirad, T
  • Wood, K
  • Wood, L
  • Worcester, E
  • Worcester, M
  • Wret, C
  • Wu, W
  • Wu, W
  • Xiao, Y
  • Yang, G
  • Yang, T
  • Yershov, N
  • Yonehara, K
  • Young, T
  • Yu, B
  • Yu, J
  • Zaki, R
  • Zalesak, J
  • Zambelli, L
  • Zamorano, B
  • Zani, A
  • Zazueta, L
  • Zeller, GP
  • Zennamo, J
  • Zeug, K
  • Zhang, C
  • Zhao, M
  • Zhivun, E
  • Zhu, G
  • Zimmerman, ED
  • Zito, M
  • Zucchelli, S
  • Zuklin, J
  • Zutshi, V
  • Zwaska, R

publication date

  • November 9, 2020