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.

Dublin Core

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  <dc:creator>Abi, B.</dc:creator>
  <dc:creator>Acciarri, R.</dc:creator>
  <dc:creator>Acero, M. A.</dc:creator>
  <dc:creator>Adamov, G.</dc:creator>
  <dc:creator>Adams, D.</dc:creator>
  <dc:creator>Adinolfi, M.</dc:creator>
  <dc:creator>Ahmad, Z.</dc:creator>
  <dc:creator>Ahmed, J.</dc:creator>
  <dc:creator>Alion, T.</dc:creator>
  <dc:creator>Monsalve, S. Alonso</dc:creator>
  <dc:creator>Alt, C.</dc:creator>
  <dc:creator>Anderson, J.</dc:creator>
  <dc:creator>Andreopoulos, C.</dc:creator>
  <dc:creator>Andrews, M. P.</dc:creator>
  <dc:creator>Andrianala, F.</dc:creator>
  <dc:creator>Andringa, S.</dc:creator>
  <dc:creator>Ankowski, A.</dc:creator>
  <dc:creator>Antonova, M.</dc:creator>
  <dc:creator>Antusch, S.</dc:creator>
  <dc:creator>Aranda-Fernandez, A.</dc:creator>
  <dc:creator>Aranda-Fernandez, A.</dc:creator>
  <dc:date>2020-01-01</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/11543</dc:identifier>
  <dc:identifier>oai:zenodo.org:11543</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>PHYSICAL REVIEW D 102(9)</dc:source>
  <dc:title>Neutrino interaction classification with a convolutional neural network in the DUNE far detector</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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