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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  "@type": "ScholarlyArticle", 
  "contributor": [], 
  "creator": [
    {
      "@type": "Person", 
      "affiliation": "KIT/ETP", 
      "name": "Mormile, Michele"
    }
  ], 
  "datePublished": "2024-12-04", 
  "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>", 
  "headline": "Searches for the rare tWZ and tW\u03b3 processes at the LHC using machine learning techniques", 
  "image": "https://publish.etp.kit.edu/static/img/logos/zenodo-gradient-round.svg", 
  "inLanguage": {
    "@type": "Language", 
    "alternateName": "eng", 
    "name": "English"
  }, 
  "keywords": [
    "Top quark", 
    "CMS", 
    "Machine Learning"
  ], 
  "name": "Searches for the rare tWZ and tW\u03b3 processes at the LHC using machine learning techniques", 
  "url": "https://publish.etp.kit.edu/record/22295"
}
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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