1 Ocak 2020 Dergi makalesi Açık Erişim

Abi, B.; Acciarri, R.; Acero, M. A.; Adamov, G.; Adams, D.; Adinolfi, M.; Ahmad, Z.; Ahmed, J.; Alion, T.; Monsalve, S. Alonso; Alt, C.; Anderson, J.; Andreopoulos, C.; Andrews, M. P.; Andrianala, F.; Andringa, S.; Ankowski, A.; Antonova, M.; Antusch, S.; Aranda-Fernandez, A.; Aranda-Fernandez, A.

Citation Style Language JSON

{
  "DOI": "10.1103/PhysRevD.102.092003", 
  "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.", 
  "author": [
    {
      "family": "Abi", 
      "given": " B."
    }, 
    {
      "family": "Acciarri", 
      "given": " R."
    }, 
    {
      "family": "Acero", 
      "given": " M. A."
    }, 
    {
      "family": "Adamov", 
      "given": " G."
    }, 
    {
      "family": "Adams", 
      "given": " D."
    }, 
    {
      "family": "Adinolfi", 
      "given": " M."
    }, 
    {
      "family": "Ahmad", 
      "given": " Z."
    }, 
    {
      "family": "Ahmed", 
      "given": " J."
    }, 
    {
      "family": "Alion", 
      "given": " T."
    }, 
    {
      "family": "Monsalve", 
      "given": " S. Alonso"
    }, 
    {
      "family": "Alt", 
      "given": " C."
    }, 
    {
      "family": "Anderson", 
      "given": " J."
    }, 
    {
      "family": "Andreopoulos", 
      "given": " C."
    }, 
    {
      "family": "Andrews", 
      "given": " M. P."
    }, 
    {
      "family": "Andrianala", 
      "given": " F."
    }, 
    {
      "family": "Andringa", 
      "given": " S."
    }, 
    {
      "family": "Ankowski", 
      "given": " A."
    }, 
    {
      "family": "Antonova", 
      "given": " M."
    }, 
    {
      "family": "Antusch", 
      "given": " S."
    }, 
    {
      "family": "Aranda-Fernandez", 
      "given": " A."
    }, 
    {
      "family": "Aranda-Fernandez", 
      "given": " A."
    }
  ], 
  "container_title": "PHYSICAL REVIEW D", 
  "id": "11543", 
  "issue": "9", 
  "issued": {
    "date-parts": [
      [
        2020, 
        1, 
        1
      ]
    ]
  }, 
  "title": "Neutrino interaction classification with a convolutional neural network in the DUNE far detector", 
  "type": "article-journal", 
  "volume": "102"
}