This dissertation presents two analyses of Higgs boson processes within the ATLAS experiment at the Large Hadron Collider (LHC). The primary analysis focuses on the legacy $VH(b\bar{b}/c\bar{c})$ process, examining the decays of the Higgs boson into bottom quarks ($H \to b\bar{b}$) and charm quarks ($H \to c\bar{c}$). Utilizing data from Run 2, a multi-variate approach with improved b-tagging algorithms was employed to increase sensitivity. The secondary analysis explores the Di-Higgs process, specifically $HH(b\bar{b})H(\gamma\gamma)$, investigating the simultaneous production of two Higgs bosons decaying into bottom quarks and photons. This analysis aims to provide insights into Higgs self-coupling, contributing to our understanding of the Higgs potential and Electroweak symmetry breaking. The performance enhancements discussed are crucial to the success of these analyses. Key improvements include advanced Flavour Tagging techniques, a study on the potential impact of 4D tracking, and a Machine Learning-based approach to the Global Particle Flow (PFlow) algorithm. Flavour Tagging, essential for identifying jets originating from heavy quarks, has seen significant advancements with the DL1 series tagger algorithms and Graph Neural Networks (GNNs), which provide a more precise classification of jet flavors. 4D tracking, incorporating precise timing information in the tracking system, shows that the object reconstruction of particles can significantly impact vertexing and $b$-tagging algorithms for HL-LHC and beyond. Global PFlow algorithms, which combine information from various detector components, enable a more accurate reconstruction of the overall event topology. The dissertation shows that these performance improvements directly reflect on the physics outcomes of the described analyses, bringing us closer to understanding the most fundamental laws of the universe.
The beauty and charm Yukawa couplings of the Higgs boson with the ATLAS detector at the LHC - 4D tracking, particle flow and jet flavour reconstruction algorithms development
SANTI, LORENZO
2024
Abstract
This dissertation presents two analyses of Higgs boson processes within the ATLAS experiment at the Large Hadron Collider (LHC). The primary analysis focuses on the legacy $VH(b\bar{b}/c\bar{c})$ process, examining the decays of the Higgs boson into bottom quarks ($H \to b\bar{b}$) and charm quarks ($H \to c\bar{c}$). Utilizing data from Run 2, a multi-variate approach with improved b-tagging algorithms was employed to increase sensitivity. The secondary analysis explores the Di-Higgs process, specifically $HH(b\bar{b})H(\gamma\gamma)$, investigating the simultaneous production of two Higgs bosons decaying into bottom quarks and photons. This analysis aims to provide insights into Higgs self-coupling, contributing to our understanding of the Higgs potential and Electroweak symmetry breaking. The performance enhancements discussed are crucial to the success of these analyses. Key improvements include advanced Flavour Tagging techniques, a study on the potential impact of 4D tracking, and a Machine Learning-based approach to the Global Particle Flow (PFlow) algorithm. Flavour Tagging, essential for identifying jets originating from heavy quarks, has seen significant advancements with the DL1 series tagger algorithms and Graph Neural Networks (GNNs), which provide a more precise classification of jet flavors. 4D tracking, incorporating precise timing information in the tracking system, shows that the object reconstruction of particles can significantly impact vertexing and $b$-tagging algorithms for HL-LHC and beyond. Global PFlow algorithms, which combine information from various detector components, enable a more accurate reconstruction of the overall event topology. The dissertation shows that these performance improvements directly reflect on the physics outcomes of the described analyses, bringing us closer to understanding the most fundamental laws of the universe.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14242/164163
URN:NBN:IT:UNIROMA1-164163