December 4, 2024 Doctoral Dissertation Open Access

Mormile, Michele

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    "description": "<p>This thesis work presents the first searches and the first evidences for the&nbsp;<span class=\"math-tex\">\\(tWZ \\)</span>&nbsp;and&nbsp;<span class=\"math-tex\">\\(tW\\gamma\\)</span>&nbsp;processes at the LHC with the CMS experiment. The analyses employ proton-proton collision data corresponding to an integrated luminosity of 138 fb<span class=\"math-tex\">\\(^{-1}\\)</span>&nbsp;collected during Run 2 of the LHC between 2016 and 2018.</p>\n\n<p>Purpose-built Machine Learning algorithms are developed in these&nbsp;searches in order&nbsp;to discriminate the rare signal processes from the large background, consisting mostly of production processes of top quark pairs in association with a <span class=\"math-tex\">\\(Z\\)</span>&nbsp;boson or a photon.</p>\n\n<p>Additionally, this thesis describes the treatments&nbsp;used to describe the modeling and simulation of the <span class=\"math-tex\">\\(tWZ \\)</span> and&nbsp;<span class=\"math-tex\">\\(tW\\gamma\\)</span>&nbsp;production process.</p>", 
    "keywords": [
      "Top quark", 
      "CMS", 
      "Machine Learning"
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    "language": "eng", 
    "publication_date": "2024-12-04", 
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      "report_number": "ETP-KA/2024-10", 
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          "affiliation": "KIT/ETP", 
          "name": "Husemmann, Ulrich"
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        {
          "affiliation": "University of Isfahan", 
          "name": "Jafari, Abideh"
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    "title": "Searches for the rare tWZ and tW\u03b3 processes at the LHC using machine learning techniques"
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