The use of machine learning in search for new physics at the ATLAS and applications to model COVID-19
| dc.contributor.author | Mathaha, Thuso Stephen | |
| dc.date.accessioned | 2024-01-24T11:45:41Z | |
| dc.date.available | 2024-01-24T11:45:41Z | |
| dc.date.issued | 2024 | |
| dc.description | A research report submitted in partial fulfilment of the requirements for the degree Master of Science to the Faculty of Science, School of Physics, University of the Witwatersrand, Johannesburg, 2023 | |
| dc.description.abstract | In this thesis, the production of a pair of top quarks in association with a heavy pseudo-scalar (A) is examined. The heavy pseudoscalar subsequently decays into another pair of top quarks, resulting in a final state of four top quarks (ttA → tttt). The ATLAS public paper Ref. [1] provided the analytical framework for this study, which aimed to investigate the four top quarks production in the multilepton final state. The study focuses on final states with two same-sign leptons of different flavours (e.g. e ±, µ±) or at most three isolated leptons (muons and electrons) without any charge requirement, as well as jets. The analysis employs a multivariate discriminant that uses twelve discriminating kinematic variables to separate the signal from the background in an effort to understand the differences between the SM and BSM production mechanisms of four top quarks. The machine learning techniques deployed for the multivariate algorithm were transferred to tackle the COVID-19 pandemic. The COVID-19 pandemic has caused significant health, social, and economic damage worldwide, with many developed countries vaccinating their citizens while African nations relied on clinical public health (CPH) strategies. Recent studies in Botswana and South Africa found age, gender, hypertension and diabetes were significant factors in disease severity, vii with the elderly population aged ≥ 60 years and those with major COVID-19 comorbidities recommended for vaccination. AI was also used to optimize vaccination roll-out strategies, targeting population groups needing | |
| dc.description.librarian | TL (2024) | |
| dc.description.sponsorship | National Research Foundation (NRF) | |
| dc.faculty | Faculty of Science | |
| dc.identifier.uri | https://hdl.handle.net/10539/37413 | |
| dc.language.iso | en | |
| dc.school | Physics | |
| dc.subject | Pseudo-scalar | |
| dc.subject | Top quarks | |
| dc.title | The use of machine learning in search for new physics at the ATLAS and applications to model COVID-19 | |
| dc.type | Dissertation |