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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    <subfield code="a">&lt;p&gt;This thesis work presents the first searches and the first evidences for the&amp;nbsp;&lt;span class="math-tex"&gt;\(tWZ \)&lt;/span&gt;&amp;nbsp;and&amp;nbsp;&lt;span class="math-tex"&gt;\(tW\gamma\)&lt;/span&gt;&amp;nbsp;processes at the LHC with the CMS experiment. The analyses employ proton-proton collision data corresponding to an integrated luminosity of 138 fb&lt;span class="math-tex"&gt;\(^{-1}\)&lt;/span&gt;&amp;nbsp;collected during Run 2 of the LHC between 2016 and 2018.&lt;/p&gt;

&lt;p&gt;Purpose-built Machine Learning algorithms are developed in these&amp;nbsp;searches in order&amp;nbsp;to discriminate the rare signal processes from the large background, consisting mostly of production processes of top quark pairs in association with a &lt;span class="math-tex"&gt;\(Z\)&lt;/span&gt;&amp;nbsp;boson or a photon.&lt;/p&gt;

&lt;p&gt;Additionally, this thesis describes the treatments&amp;nbsp;used to describe the modeling and simulation of the &lt;span class="math-tex"&gt;\(tWZ \)&lt;/span&gt; and&amp;nbsp;&lt;span class="math-tex"&gt;\(tW\gamma\)&lt;/span&gt;&amp;nbsp;production process.&lt;/p&gt;</subfield>
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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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