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  <ref-type name="Journal Article">17</ref-type>
  <contributors>
    <authors>
      <author>Kisel, Ivan</author>
      <author>Lakos, Robin</author>
      <author>Zischka, Gianna</author>
    </authors>
    <subsidiary-authors>
      <author>FHF</author>
    </subsidiary-authors>
  </contributors>
  <titles>
    <title>Deep-Learning-Based Optimization of the Signal/Background Ratio for $\Lambda$ Particles in the CBM Experiment at FAIR</title>
    <secondary-title>Algorithms</secondary-title>
  </titles>
  <periodical>
    <full-title>Algorithms</full-title>
  </periodical>
  <publisher>MDPI</publisher>
  <pub-location>Basel</pub-location>
  <isbn>1999-4893</isbn>
  <electronic-resource-num>10.3390/a18040229</electronic-resource-num>
  <pages>229</pages>
  <number>4</number>
  <volume>18</volume>
  <abstract/>
  <notes>
    <note>This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). This research was partly funded by the Federal Ministry of Education and Research [grant numbers 01IS21092, 05P24RF3, 05P24RF7] ; </note>
  </notes>
  <label>PUB:(DE-HGF)16, ; 0, ; </label>
  <keywords/>
  <accession-num/>
  <work-type>Journal Article</work-type>
  <dates>
    <pub-dates>
      <year>2025</year>
    </pub-dates>
  </dates>
  <accession-num>GSI-2026-00251</accession-num>
  <year>2025</year>
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    <related-urls>
      <url>https://repository.gsi.de/record/364072</url>
      <url>https://doi.org/10.3390/a18040229</url>
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