Doctoral Dissertation Open Access

Searches for the rare tWZ and tWγ processes at the LHC using machine learning techniques

Mormile, Michele

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{
  "abstract": "<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>", 
  "author": [
    {
      "family": "Mormile, Michele"
    }
  ], 
  "id": "22295", 
  "issued": {
    "date-parts": [
      [
        2024, 
        12, 
        4
      ]
    ]
  }, 
  "language": "eng", 
  "title": "Searches for the rare tWZ and tW\u03b3 processes at the LHC using machine learning techniques", 
  "type": "thesis"
}
Publication date:
December 4, 2024
Keyword(s):
Top quark CMS Machine Learning
Awarding University:
Karlsruhe Institute of Technology (KIT)
Report Number:
ETP-KA/2024-10
Communities:

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