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Searching for Beyond-the-Standard-Model Physics Using Signatures with Tau Leptons in the Final State at the ATLAS Experiment
The main focus of this thesis is the search for heavy, neutral Higgs bosons decaying to two tau leptons. The existence of such bosons is predicted by certain Beyond-the-Standard-Model (BSM) models such as Two-Higgs-Doublet Models and the Minimal Supersymmetric Standard Model (MSSM). The search was c...
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Lenguaje: | eng |
Publicado: |
2022
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Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2803965 |
Sumario: | The main focus of this thesis is the search for heavy, neutral Higgs bosons decaying to two tau leptons. The existence of such bosons is predicted by certain Beyond-the-Standard-Model (BSM) models such as Two-Higgs-Doublet Models and the Minimal Supersymmetric Standard Model (MSSM). The search was conducted in the mass range between 200 GeV and 2.5 TeV using proton-proton collision data collected by the ATLAS detector at the LHC in the years 2015-2018 at a center-of-mass energy of 13 TeV. The analysis was split into two rounds. In the first round, the analysis group followed the same strategy as the one used in a previous paper analyzing only 2015 and 2016 data. The author’s main task within the analysis group was to validate the estimation of background contributions with QCD jets misidentified as tau leptons in events where one of the tau leptons decays to leptons and the other one to hadrons and a neutrino (‘$\tau_\text{lep}\tau_\text{had}$ decay channel’). In this analysis round, no evidence of new bosons was found, and new exclusion limits were set on the BSM Higgs boson production cross-section times the branching ratio to two tau leptons (model-independent limit) and on the phase space of two MSSM benchmark scenarios (model-dependent limits). In the second analysis round, improvements were introduced to the analysis strategy. The author’s task was to develop machine learning models for discriminating between signal and background events in the $\tau_\text{lep}\tau_\text{had}$ decay channel. The goal was to obtain a better performance compared to the standard final discriminating variable, the mass variable $m_T^\text{tot}$. Boosted decision trees and mass-parameterized neural networks were trained using mass variables and kinematic variables. Ultimately, the best performance was obtained with boosted decision trees, improving the expected model-independent exclusion limits by up to 67.5% and 28.6% for low-signal-mass events with and without $b$-jets in the final state, respectively. At the time of submitting this thesis, the second analysis round was ongoing. Two additional areas of focus are presented in this thesis. The first is a theoretical examination of the possible link between the Higgs mass fine-tuning problem and the cosmological constant problem, taking into consideration Veltman and Pauli divergence-cancelling conditions, as well as the Standard Model effective field theory. The possible role of BSM Higgs bosons is discussed here. This part of the thesis is the result of a collaboration between the author and Steven Bass. The second area of focus is the validation of ATLAS Monte Carlo samples containing tau leptons. Validation studies included polarization measurements, branching ratio and kinematic variable checks, and comparisons of input variables for the ATLAS tau-lepton-identifying boosted decision tree model. |
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