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Title:A search for the ttH (H → bb) channel at the Large Hadron Collider with the ATLAS detector using a matrix element method
Author(s):Basye, Austin Thomas
Director of Research:Liss, Tony M.
Doctoral Committee Chair(s):El-Khadra, Aida X
Doctoral Committee Member(s):Eckstein, James N.; Martinez Outschoorn, Verena I
Department / Program:Physics
Discipline:Physics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Higgs Boson
Large Hadron Collider (LHC)
A Toroidal LHC Apparatus (ATLAS)
8 trillion electron volt (TeV)
ttH
H to bb
Matrix Element Method
Neural Network
Boosted Decision Tree
Standard Model
Abstract:A matrix element method analysis of the Standard Model Higgs boson, produced in association with two top quarks decaying to the lepton-plus-jets channel is presented. Based on 20.3 fb−1 of √s=8 TeV data, produced at the Large Hadron Collider and collected by the ATLAS detector, this analysis utilizes multiple advanced techniques to search for tt ̄H signatures with a 125 GeV Higgs boson decaying to two b-quarks. After categorizing selected events based on their jet and b-tag multiplicities, signal rich regions are analyzed using the matrix element method. Resulting variables are then propagated to two parallel multivariate analyses utilizing Neural Networks and Boosted Decision Trees respectively. As no significant excess is found, an observed (expected) limit of 3.4 (2.2) times the Standard Model cross-section is determined at 95% confidence, using the CLs method, for the Neural Network analysis. For the Boosted Decision Tree analysis, an observed (expected) limit of 5.2 (2.7) times the Standard Model cross-section is determined at 95% confidence, using the CLs method. Corresponding unconstrained fits of the Higgs boson signal strength to the observed data result in the measured signal cross-section to Standard Model cross-section prediction of μ = 1.2 ± 1.3(total) ± 0.7(stat.) for the Neural Network analysis, and μ = 2.9 ± 1.4(total) ± 0.8(stat.) for the Boosted Decision Tree analysis.
Issue Date:2015-07-07
Type:Thesis
URI:http://hdl.handle.net/2142/88000
Rights Information:Copyright 2015 Austin T. Basye
Date Available in IDEALS:2015-09-29
Date Deposited:August 201


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