Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21081
Title: Search for the standard model production of a single top quark in association with a Z0 boson using machine learning techniques
Authors: Hoad, Corin J. K.
Advisors: Cole, J
Keywords: high energy physics;particle physics;boosted decision tree;multilayer perceptron;Gaussian process
Issue Date: 2020
Publisher: Brunel University London
Abstract: This thesis presents a search for the production of a single top quark in association with a Z0 boson in the dileptonic decay channel as predicted by the Standard Model. The search uses 77.8 fbβˆ’1 of data from βˆšπ‘  = 13 TeV proton– proton collisions collected by the Compact Muon Solenoid experiment at the Large Hadron Collider during the 2016–2017 data-taking period. The search identified events containing a Z0 boson decay by requiring two opposite-sign same-flavour electrons or muons in the final state with invariant mass compatible with the nominal Z0 boson mass. Products of the top quark decay were identified using techniques developed to identify jets originating from bottom quarks and searching for a jet pair with invariant mass compatible with the WΒ± boson mass. Machine learning techniques were used to further discriminate the signal process from background events. A study was carried out, comparing the performance of boosted decision trees with hyperparameters optimised using a Gaussian process and multi-layer perceptrons on this problem. The boosted decision trees were found to outperform the multi-layer perceptrons. A signal strength of π‘ŸΜ‚ = 6.52+2.30 βˆ’2.05 was observed, where π‘ŸΜ‚ = 1.0 corresponds to the Standard Model expectation. The corresponding observed (expected) significance is 3.12𝜎 (0.48𝜎).
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.
URI: http://bura.brunel.ac.uk/handle/2438/21081
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Theses

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