21 Kasım 2024 Dergi makalesi Açık Erişim

Celik, Ali

MARC21 XML

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">user-scoap3-turkiye</subfield>
    <subfield code="o">oai:aperta.ulakbim.gov.tr:274563</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;Detecting beyond the Standard Model (BSM) signals in high-energy particle collisions presents significant challenges due to complex data and the need to differentiate rare signal events from the Standard Model (SM) backgrounds. This study investigates the efficacy of deep learning models, specifically Deep Neural Networks (DNNs) and Graph Neural Networks (GNNs), in classifying particle collision events as either BSM signal or background. The research utilized a dataset comprising 214,000 SM background and 10,755 BSM events. To address class imbalance, an undersampling method was employed, resulting in balanced classes. Three models were developed and compared: a DNN and two GNN variants with different graph construction methods. All models demonstrated high performance, achieving Area Under the Receiver Operating Characteristic curve (AUC) values exceeding \(94\%\). While the DNN model slightly outperformed GNNs across various metrics, both GNN approaches showed comparable results despite different graph structures. The GNNs' ability to explicitly capture inter-particle relationships within events highlights their potential for BSM signal detection.&lt;/p&gt;</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">article</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="a">Creative Commons Attribution</subfield>
    <subfield code="u">http://www.opendefinition.org/licenses/cc-by</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="a">Celik, Ali</subfield>
    <subfield code="u">Department of Physics, Burdur Mehmet Akif Ersoy University, Burdur, Turkey</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="z">md5:f4f60805546ae228e6037063ecab58cb</subfield>
    <subfield code="s">1852187</subfield>
    <subfield code="u">https://aperta.ulakbim.gov.trrecord/274563/files/APhysPolB.55.10-A2.pdf</subfield>
  </datafield>
  <controlfield tag="005">20250417102100.0</controlfield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2024-11-21</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5506/APhysPolB.55.10-A2</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Deep Learning Approaches for BSM Physics: Evaluating DNN and GNN Performance in Particle Collision Event Classification</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="4">
    <subfield code="v">55</subfield>
    <subfield code="p">Acta Physica Polonica B</subfield>
    <subfield code="n">10</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <controlfield tag="001">274563</controlfield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-scoap3-turkiye</subfield>
  </datafield>
</record>