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Simultaneous cross section measurements of top quark-antiquark pair production with additional heavy flavor jets at the CMS experiment

Pfeffer, Emanuel Lorenz ORCID iD icon 1
1 Institut für Experimentelle Teilchenphysik (ETP), Karlsruher Institut für Technologie (KIT)

Abstract:

This thesis presents a simultaneous cross section measurement of top quark-antiquark pair (tt¯) production in association with b jets, c jets, a Higgs boson, or a Z boson in the H/Zbb¯ decay mode with exactly two charged leptons (e,μ), targeting the dilepton tt¯ decay channel.
The measurement is performed at the CMS experiment at the CERN LHC in proton-proton collisions at a center-of-mass energy of 13TeV and the analyzed data corresponds to an integrated luminosity of approximately 60fb1.
Collision events are modeled as a mathematical graph structure and processed using graph transformer neural network architectures based on multi-head attention mechanisms to perform multi-class classification.
The measured cross sections are parameterized as signal strength parameters relative to the cross sections predicted by the SM.
Four signal strength parameters are simultaneously extracted in a maximum likelihood fit to data and result to μtt¯Bobs=0.980.25+0.34, μtt¯Cobs=0.740.41+0.41, μtt¯Hobs=0.890.93+0.95, and μtt¯Zobs=1.281.06+1.15, corresponding to an observed (expected) significance of 15σ (16σ) for tt¯B, 1.8σ (2.9σ) for tt¯C, 1.0σ (1.1σ) for tt¯H, and 1.2σ (1.0σ) for tt¯Z compared to the SM prediction without these processes.
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Zugehörige Institution(en) am KIT Institut für Experimentelle Teilchenphysik (ETP)
Publikationstyp Hochschulschrift
Publikationsdatum 29.01.2025
Sprache Englisch
Identifikator KITopen-ID: 1000178528
Verlag Karlsruher Institut für Technologie (KIT)
Umfang viii, 229 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Physik (PHYSIK)
Institut Institut für Experimentelle Teilchenphysik (ETP)
Prüfungsdatum 24.01.2025
Schlagwörter CERN, LHC, CMS, particle physics, top physics, Higgs physics, cross section, Artificial Intelligence, Machine Learning
Referent/Betreuer Husemann, Ulrich
Müller, Thomas

Volltext §
DOI: 10.5445/IR/1000178528
Veröffentlicht am 29.01.2025
Seitenaufrufe: 177
seit 29.01.2025
Downloads: 124
seit 29.01.2025
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