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Top Quark Studies with the CMS Experiment: Rare Process and Precision Measurements

Top quark is the heaviest known fundamental particle. In this thesis I present two studies on the top quark. The first study is a combined measurement of the mass and the decay width of the top quark in di-leptonic ttbar decay events resulting from proton-proton collisions at center-of-mass energy $...

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Detalles Bibliográficos
Autor principal: Zhang, Wenyu
Lenguaje:eng
Publicado: 2022
Materias:
Acceso en línea:http://cds.cern.ch/record/2809600
Descripción
Sumario:Top quark is the heaviest known fundamental particle. In this thesis I present two studies on the top quark. The first study is a combined measurement of the mass and the decay width of the top quark in di-leptonic ttbar decay events resulting from proton-proton collisions at center-of-mass energy $\sqrt{s}$ = 13 TeV. Data was collected with the CMS detector during Run 2 of the CERN LHC in 2016. Invariant mass of lepton and b-jet pairs, called mlb, is used as observable. Events are categorized by lepton flavor (ee, e$\mu$, $\mu\mu$), b-quark jet multiplicity, and the transverse momentum of the lepton b-jet pairs. Simulated expectations for different top mass and width scenarios are compared to the data via a profile likelihood method. The second study is a search for the rare production process $t\bar{t} t\bar{t}$. We study the single lepton final state in which one of the four top quarks decays leptonically, and three top quarks decay hadronically. We analyze the proton-proton collision data taken in 2016, 2017 and 2018. The distributions of $H_T$, the scalar sum of jet transverse momentum $p_T$ is used in order to discriminate signal from background. In this analysis, we make use of a resolved top tagging algorithm to tag the low $p_T$ top quarks that decay hadronically. Events are categorized by lepton flavor, number of AK4 jets, number of b-tagged jets, and number of resolved top-tagged jets. We set 95\% upper limits on the $t\bar{t} t\bar{t}$ production cross section. We further improve the sensitivity with a discriminant constructed by a multivariate analysis based on the Boosted Decision Tree (BDT) method.