Study Of The Higgs Boson Pair Production With Run 2 Atlas Data Using Cut Based And Machine Learning Approaches
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Study of the Higgs Boson Pair Production with Run-2 ATLAS Data Using Cut-based and Machine Learning Approaches
After the groundbreaking discovery of the Higgs boson, associated with the Higgs field responsible for imparting mass to other particles, by the CMS and ATLAS experiments at CERN, a promising opportunity has emerged to delve into its properties and explore new physics beyond the standard model (SM). A crucial property to be measured is the Higgs self-coupling, which directly probes the test of the Higgs potential and electroweak symmetry breaking. Higgs pair (HH) production at the Large Hadron Collider (LHC) provides a direct probe for measuring the Higgs self-coupling parameter. In this analysis, we focus on HH production through Vector Boson Fusion (VBF), which is the second-leading HH production channel. We consider the decay channel HH->bbtautau, where one tau decays leptonically and the other tau decays hadronically. The analysis uses the dataset with an integrated luminosity of 140 fb^{-1} collected by the ATLAS detector at \sqrt{s} = 13 TeV. We introduce two methods to perform the optimization, which are cut-based studies and machine learning studies, specifically using Boosted Decision Trees (BDTs), by comparing the significance and upper limits set on the cross-section. The expected upper limits by BDTs (cut-based studies), considering only statistical uncertainties, are set at the 95% confidence level on the SM HH production cross section, which is 77.7 (212.0) times the SM prediction.
Higgs Boson Decays into a Pair of Bottom Quarks
The discovery in 2012 of the Higgs boson at the Large Hadron Collider (LHC) represents a milestone for the Standard Model (SM) of particle physics. Most of the SM Higgs production and decay rates have been measured at the LHC with increased precision. However, despite its experimental success, the SM is known to be only an effective manifestation of a more fundamental description of nature. The scientific research at the LHC is strongly focused on extending the SM by searching, directly or indirectly, for indications of New Physics. The extensive physics program requires increasingly advanced computational and algorithmic techniques. In the last decades, Machine Learning (ML) methods have made a prominent appearance in the field of particle physics, and promise to address many challenges faced by the LHC. This thesis presents the analysis that led to the observation of the SM Higgs boson decay into pairs of bottom quarks. The analysis exploits the production of a Higgs boson associated with a vector boson whose signatures enable efficient triggering and powerful background reduction. The main strategy to maximise the signal sensitivity is based on a multivariate approach. The analysis is performed on a dataset corresponding to a luminosity of 79.8/fb collected by the ATLAS experiment during Run-2 at a centre-of-mass energy of 13 TeV. An excess of events over the expected background is found with an observed (expected) significance of 4.9 (4.3) standard deviation. A combination with results from other \Hbb searches provides an observed (expected) significance of 5.4 (5.5). The corresponding ratio between the signal yield and the SM expectation is 1.01 +- 0.12 (stat.)+ 0.16-0.15(syst.). The 'observation' analysis was further extended to provide a finer interpretation of the V H(H → bb) signal measurement. The cross sections for the VH production times the H → bb branching ratio have been measured in exclusive regions of phase space. These measurements are used to search for possible deviations from the SM with an effective field theory approach, based on anomalous couplings of the Higgs boson. The results of the cross-section measurements, as well as the constraining of the operators that affect the couplings of the Higgs boson to the vector boson and the bottom quarks, have been documented and discussed in this thesis. This thesis also describes a novel technique for the fast simulation of the forward calorimeter response, based on similarity search methods. Such techniques constitute a branch of ML and include clustering and indexing methods that enable quick and efficient searches for vectors similar to each other. The new simulation approach provides optimal results in terms of detector resolution response and reduces the computational requirements of a standard particles simulation.
ATLAS Measurements of the Higgs Boson Coupling to the Top Quark in the Higgs to Diphoton Decay Channel
Author: Jennet Elizabeth Dickinson
language: en
Publisher: Springer Nature
Release Date: 2021-11-16
During Run 2 of the Large Hadron Collider, the ATLAS experiment recorded proton-proton collision events at 13 TeV, the highest energy ever achieved in a collider. Analysis of this dataset has provided new opportunities for precision measurements of the Higgs boson, including its interaction with the top quark. The Higgs-top coupling can be directly probed through the production of a Higgs boson in association with a top-antitop quark pair (ttH). The Higgs to diphoton decay channel is among the most sensitive for ttH measurements due to the excellent diphoton mass resolution of the ATLAS detector and the clean signature of this decay. Event selection criteria were developed using novel Machine Learning techniques to target ttH events, yielding a precise measurement of the ttH cross section in the diphoton channel and a 6.3 $\sigma$ observation of the ttH process in combination with other decay channels, as well as stringent limits on CP violation in the Higgs-top coupling.