Higgs boson studies: associated production with a vector boson and decay into b-quarks using the ATLAS Run-2 dataset

Spiteri, Dwayne (2021) Higgs boson studies: associated production with a vector boson and decay into b-quarks using the ATLAS Run-2 dataset. PhD thesis, University of Glasgow.

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On the 4th July 2012, the Standard Model of Particle Physics received further validation with the discovery of the Higgs boson; ushering in a new age of Higgs physics. This thesis presents some of my contributions to the current research in this field as a member of the ATLAS experiment at CERN. It explains how the ATLAS experiment fits within the CERN accelerator complex and the structure of the ATLAS detector, leading to a description of some of the work that I did towards the upgrade of its hardware, and my studies on the reconstruction of tracks in the detector.

The thesis goes on to present the results of two analyses I worked on: The VH, H -> bb (read: VHbb) Resolved Analysis and the VH, H -> bb Boosted Analysis. Specific attention will be drawn to my fit studies in the Resolved Analysis, and the new trigger strategy I designed for the Boosted Analysis.

The analyses make use of 139fb^-1 of proton-proton collision data at the centre-of-mass energy of 13 TeV, collected by the ATLAS detector between 2015 and 2018, to observe the Higgs boson decay to b-quarks via associated Vector Boson production (VH, H -> bb), and go on to provide differential cross-section measurements in bins of the transverse momentum of the vector bosons.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Keywords: Physics, Particle Physics, Particle Physics Theory, Standard Model, SM, Beyond Standard Model Physics, BSM, Higgs Boson, Associated Production, Higgsstrahlung, Vector Bosons, b-quarks, LHC, CERN, The ATLAS Experiment, ATLAS Upgrade, Silicon Technologies, Monte Carlo Simulations, Tracking. Fake Tracks, Combined Performance, Statistical Analysis, Multivariate Analysis, MVA, Boosted Decision Trees, BDT, Simple Template Cross Sections, STXS, Machine Learning, Statistics, Statistical Fitting, Analysis Optimisation.
Subjects: Q Science > QC Physics
Colleges/Schools: College of Science and Engineering > School of Physics and Astronomy
Supervisor's Name: Robson, Professor Aidan
Date of Award: 2021
Depositing User: Mr Dwayne Spiteri
Unique ID: glathesis:2021-81897
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 08 Jan 2021 08:45
Last Modified: 12 Jan 2021 15:05
Thesis DOI: 10.5525/gla.thesis.81897
URI: http://theses.gla.ac.uk/id/eprint/81897

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