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.

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{
  "@context": "https://schema.org/", 
  "@id": 11543, 
  "@type": "ScholarlyArticle", 
  "creator": [
    {
      "@type": "Person", 
      "affiliation": "Univ Oxford, Oxford OX1 3RH, England", 
      "name": "Abi, B."
    }, 
    {
      "@type": "Person", 
      "affiliation": "Fermilab Natl Accelerator Lab, POB 500, Batavia, IL 60510 USA", 
      "name": "Acciarri, R."
    }, 
    {
      "@type": "Person", 
      "affiliation": "Univ Atlantico, Atlantico, Colombia", 
      "name": "Acero, M. A."
    }, 
    {
      "@type": "Person", 
      "affiliation": "Georgian Tech Univ, Tbilisi, Georgia", 
      "name": "Adamov, G."
    }, 
    {
      "@type": "Person", 
      "affiliation": "Brookhaven Natl Lab, Upton, NY 11973 USA", 
      "name": "Adams, D."
    }, 
    {
      "@type": "Person", 
      "affiliation": "Univ Bristol, Bristol BS8 1TL, Avon, England", 
      "name": "Adinolfi, M."
    }, 
    {
      "@type": "Person", 
      "affiliation": "Ctr Variable Energy Cyclotron, Kolkata 700064, W Bengal, India", 
      "name": "Ahmad, Z."
    }, 
    {
      "@type": "Person", 
      "affiliation": "Univ Warwick, Coventry CV4 7AL, W Midlands, England", 
      "name": "Ahmed, J."
    }, 
    {
      "@type": "Person", 
      "affiliation": "Univ Sussex, Brighton BN1 9RH, E Sussex, England", 
      "name": "Alion, T."
    }, 
    {
      "@type": "Person", 
      "affiliation": "CERN, European Org Nucl Res, CH-1211 Meyrin, Switzerland", 
      "name": "Monsalve, S. Alonso"
    }, 
    {
      "@type": "Person", 
      "name": "Alt, C."
    }, 
    {
      "@type": "Person", 
      "name": "Anderson, J."
    }, 
    {
      "@type": "Person", 
      "name": "Andreopoulos, C."
    }, 
    {
      "@type": "Person", 
      "affiliation": "Fermilab Natl Accelerator Lab, POB 500, Batavia, IL 60510 USA", 
      "name": "Andrews, M. P."
    }, 
    {
      "@type": "Person", 
      "affiliation": "Univ Antananarivo, Antananarivo 101, Madagascar", 
      "name": "Andrianala, F."
    }, 
    {
      "@type": "Person", 
      "name": "Andringa, S."
    }, 
    {
      "@type": "Person", 
      "affiliation": "SLAC Natl Accelerator Lab, Menlo Pk, CA 94025 USA", 
      "name": "Ankowski, A."
    }, 
    {
      "@type": "Person", 
      "affiliation": "Inst Fis Corpuscular, Valencia 46980, Spain", 
      "name": "Antonova, M."
    }, 
    {
      "@type": "Person", 
      "affiliation": "Univ Basel, CH-4056 Basel, Switzerland", 
      "name": "Antusch, S."
    }, 
    {
      "@type": "Person", 
      "affiliation": "Univ Colima, Colima, Mexico", 
      "name": "Aranda-Fernandez, A."
    }, 
    {
      "@type": "Person", 
      "affiliation": "Univ Colima, Colima, Mexico", 
      "name": "Aranda-Fernandez, A."
    }
  ], 
  "datePublished": "2020-01-01", 
  "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.", 
  "headline": "Neutrino interaction classification with a convolutional neural network in the DUNE far detector", 
  "identifier": 11543, 
  "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", 
  "license": "http://www.opendefinition.org/licenses/cc-by", 
  "name": "Neutrino interaction classification with a convolutional neural network in the DUNE far detector", 
  "url": "https://aperta.ulakbim.gov.tr/record/11543"
}