Following the discovery of the Higgs boson in 2012 and the observation in 2018 of its production in association with one or two top quarks (tH and ttH), the study of tH and ttH production has become one of the main objectives of the LHC physics program. These cross sections provide direct constraints on the Yukawa coupling between the Higgs boson and the top quark, a key parameter for both Standard Model and Beyond Standard Model theories. In this thesis, tH and ttH production is studied using data from proton-proton collisions at a center-of-mass energy of 13.6 TeV, collected with the CMS detector. The production rates are measured in multiple signal regions, while the main background processes are constrained in dedicated control regions. A simultaneous fit is performed to estimate the total signal rate. To maximise the sensitivity to the tH and ttH processes in the signal regions and to the main background processes in each dedicated control region, advanced machine learning techniques are developed to classify preselected events into categories.

Following the discovery of the Higgs boson in 2012 and the observation in 2018 of its production in association with one or two top quarks (tH and ttH), the study of tH and ttH production has become one of the main objectives of the LHC physics program. These cross sections provide direct constraints on the Yukawa coupling between the Higgs boson and the top quark, a key parameter for both Standard Model and Beyond Standard Model theories. In this thesis, tH and ttH production is studied using data from proton-proton collisions at a center-of-mass energy of 13.6 TeV, collected with the CMS detector. The production rates are measured in multiple signal regions, while the main background processes are constrained in dedicated control regions. A simultaneous fit is performed to estimate the total signal rate. To maximise the sensitivity to the tH and ttH processes in the signal regions and to the main background processes in each dedicated control region, advanced machine learning techniques are developed to classify preselected events into categories.

Search for associated production of Higgs boson and top quark pair in multilepton events using the CMS detector

DE PICCOLI, JACOPO
2024/2025

Abstract

Following the discovery of the Higgs boson in 2012 and the observation in 2018 of its production in association with one or two top quarks (tH and ttH), the study of tH and ttH production has become one of the main objectives of the LHC physics program. These cross sections provide direct constraints on the Yukawa coupling between the Higgs boson and the top quark, a key parameter for both Standard Model and Beyond Standard Model theories. In this thesis, tH and ttH production is studied using data from proton-proton collisions at a center-of-mass energy of 13.6 TeV, collected with the CMS detector. The production rates are measured in multiple signal regions, while the main background processes are constrained in dedicated control regions. A simultaneous fit is performed to estimate the total signal rate. To maximise the sensitivity to the tH and ttH processes in the signal regions and to the main background processes in each dedicated control region, advanced machine learning techniques are developed to classify preselected events into categories.
2024
Search for associated production of Higgs boson and top quark pair in multilepton events using the CMS detector
Following the discovery of the Higgs boson in 2012 and the observation in 2018 of its production in association with one or two top quarks (tH and ttH), the study of tH and ttH production has become one of the main objectives of the LHC physics program. These cross sections provide direct constraints on the Yukawa coupling between the Higgs boson and the top quark, a key parameter for both Standard Model and Beyond Standard Model theories. In this thesis, tH and ttH production is studied using data from proton-proton collisions at a center-of-mass energy of 13.6 TeV, collected with the CMS detector. The production rates are measured in multiple signal regions, while the main background processes are constrained in dedicated control regions. A simultaneous fit is performed to estimate the total signal rate. To maximise the sensitivity to the tH and ttH processes in the signal regions and to the main background processes in each dedicated control region, advanced machine learning techniques are developed to classify preselected events into categories.
Higgs boson
top quark
machine learning
hadron collisions
particle physics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/91189