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Novel b-jet Analyses for Beyond the Standard Model Higgs Bosons at the LHC

Novel b-jet Analyses for Beyond the Standard Model Higgs Bosons at the LHC
Novel b-jet Analyses for Beyond the Standard Model Higgs Bosons at the LHC
Despite the many successes of the LHC since running began in 2008 - such as the discovery of the Higgs Boson in 2012 - the hunt for new physics beyond the Standard Model (SM) remains elusive. One particularly appealing extension to the SM comes in the form of Two-Higgs-Doublet-Models (2HDMs), which provide an enriched scalar sector of additional Higgs particles (the CP-even H and h, CP-odd A, and charged H±). Where kinematically possible, interactions between these Higgses can lead to high b-jet multiplicity final states via Hhhbb ¯b ¯b decays. In this thesis, various novel approaches to observing such new physics are considered. An alternative to traditional jet clustering algorithms, using a variable-R cone dependent on jet pT , is shown to increase the potential signal significance when modelled against the leading backgrounds. Furthermore, the use of high level machine learning is investigated. By mapping pT -weighted pixels in a detector into images, we build a convolutional neural network (CNN) to classify wide cone b-jets in signal events coming from 2HDM decays, against the leading backgrounds. Finally, we present a novel approach to jet reconstruction using spectral clustering machine learning techniques, and compare the performance with the currently well established methods in use at the LHC.
University of Southampton
Ford, Billy, George
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Ford, Billy, George
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Moretti, Stefano
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Ford, Billy, George (2021) Novel b-jet Analyses for Beyond the Standard Model Higgs Bosons at the LHC. University of Southampton, Doctoral Thesis, 134pp.

Record type: Thesis (Doctoral)

Abstract

Despite the many successes of the LHC since running began in 2008 - such as the discovery of the Higgs Boson in 2012 - the hunt for new physics beyond the Standard Model (SM) remains elusive. One particularly appealing extension to the SM comes in the form of Two-Higgs-Doublet-Models (2HDMs), which provide an enriched scalar sector of additional Higgs particles (the CP-even H and h, CP-odd A, and charged H±). Where kinematically possible, interactions between these Higgses can lead to high b-jet multiplicity final states via Hhhbb ¯b ¯b decays. In this thesis, various novel approaches to observing such new physics are considered. An alternative to traditional jet clustering algorithms, using a variable-R cone dependent on jet pT , is shown to increase the potential signal significance when modelled against the leading backgrounds. Furthermore, the use of high level machine learning is investigated. By mapping pT -weighted pixels in a detector into images, we build a convolutional neural network (CNN) to classify wide cone b-jets in signal events coming from 2HDM decays, against the leading backgrounds. Finally, we present a novel approach to jet reconstruction using spectral clustering machine learning techniques, and compare the performance with the currently well established methods in use at the LHC.

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Published date: December 2021

Identifiers

Local EPrints ID: 457027
URI: http://eprints.soton.ac.uk/id/eprint/457027
PURE UUID: dfff1b70-2099-48f8-a338-b14a7db35b7f
ORCID for Stefano Moretti: ORCID iD orcid.org/0000-0002-8601-7246

Catalogue record

Date deposited: 19 May 2022 16:49
Last modified: 20 May 2022 01:36

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Contributors

Author: Billy, George Ford
Thesis advisor: Stefano Moretti ORCID iD

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