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  <contributors>
    <authors>
      <author>Malyshkin, Yury</author>
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      <author>FFN</author>
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  <titles>
    <title>Novel Fitting Approach Based on a Neural Network for JUNO</title>
    <secondary-title>The European physical journal / Web of Conferences</secondary-title>
    <secondary-title>27th International Conference on Computing in High Energy and Nuclear Physics</secondary-title>
  </titles>
  <periodical>
    <full-title>The European physical journal / Web of Conferences</full-title>
  </periodical>
  <publisher>EDP Sciences</publisher>
  <pub-location>Les Ulis</pub-location>
  <pub-location>Krakow, Poland</pub-location>
  <isbn>2100-014X</isbn>
  <electronic-resource-num>10.1051/epjconf/202533701219</electronic-resource-num>
  <pages>01219</pages>
  <number/>
  <volume>337337</volume>
  <abstract/>
  <notes>
    <note>This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/). ; </note>
  </notes>
  <label>PUB:(DE-HGF)8, ; PUB:(DE-HGF)16, ; 0, ; </label>
  <keywords/>
  <accession-num/>
  <work-type>Contribution to a conference proceedings</work-type>
  <dates>
    <pub-dates>
      <year>2025</year>
    </pub-dates>
    <date>2024-10-19 - 2024-10-24</date>
  </dates>
  <accession-num>GSI-2025-01361</accession-num>
  <date>2024-10-19 - 2024-10-24</date>
  <year>2025</year>
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      <url>https://doi.org/10.1051/epjconf/202533701219</url>
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