WIReDSpace

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Communities in WIReDSpace

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Now showing 1 - 5 of 19

Recent Submissions

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    Exploring shifts in the threshold for reporting on extreme temperature events over the past century in South Africa
    (University of the Witwatersrand, Johannesburg, 2026) Holtz, Cassia; Fitchett, Jennifer M.
    This research aims to better understand how the heat wave threshold has changed in South Africa over the past century. Based in practices of historical climatology, a mixed methods approach was used to explore both purely quantitative meteorological data and more documentary evidence from archival newspapers, namely the Rand Daily Mail and the Business Day between the years 1903-2023. By exploring both sides of heat waves, the meteorological data as well as human perception around heat waves, this study aimed to explore how heat waves and their perception by people in South Africa have changed over time. This study identified 235 heat waves across 12 locations throughout South Africa that were reported in newspapers and had coinciding SAWS meteorological data. The newspaper reports were used to explore how people and journalists perceive high temperatures and write and talk about heat waves. Over the course of the last century, newspaper reported heat wave thresholds have not changed in any statistically significant way. This has implications as climate change causes a shift towards warmer mean annual temperatures and an increase in extremely hot days, meaning that there will be more days above this perceived threshold where people in South Africa will be more vulnerable to thermal stress. A total of 395 heat waves that align with the SAWS heat wave definition were also explored, also showing no statistically significant change over time. This exploratory study of historical heat waves across a range of locations in South Africa demonstrates the richness of documentary evidence of climate and weather changes through newspaper reporting. It also explores the ways in which people have always had an interest in the weather and climatic changes and why they are happening. In a warming world where heat waves will increase in intensity, understanding how they are changing and whether human understanding of them is changing too will be crucial to public health preparedness in South Africa.
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    Hunting the hidden: exploring different dark matter scenarios
    (University of the Witwatersrand, Johannesburg, 2025) Prat, Clarisse; Kar, Deepak
    This thesis explores searches for Dark Matter at colliders through a phenomenological and experimental approach. Following an introduction to the Standard Model and Beyond the Standard Model theories relevant to the work in this thesis, the Large Hadron Collider and the ATLAS experiment are introduced. Important components of event generation are discussed, along with specifics of the event generators and analysis tools used. The first work completed and shown is work done as a Summer Student at CERN in 2024, working on the ATLAS Trigger Menu. This is followed by the work done as part of an ATLAS search for a new hypothetical particle, using simulated events based on Run 2 data and 139 fb−1 of data, as well as a discussion of the model it is based on. The last two parts discuss phenomenological studies performed over the past months, and are relevant in the context of Dark Matter searches in colliders, specifically at the Large Hadron Collider.
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    Solving differential equations using learning-based techniques
    (University of the Witwatersrand, Johannesburg, 2026) Ilung, Tshibang Gideon; Byron, Jacobs
    Physics-based machine learning models are an emerging approach in machine learning to solve differential equations. These models integrate the known physics of a system into the neural network training process, enhancing the efficiency of so- lutions to differential equations. This study will explore the capability of these mod- els to tackle differential equations across various fields, including fluid dynamics, solid mechanics, and heat transfer. It will feature comparative analyses of differ- ent optimisation algorithms, determining under which conditions an algorithm is computationally more effective. In addition, the study will examine the impact of various hyperparameters and training strategies on the performance of these mod- els. The potential of physics-based machine learning models lies in their capability to provide accurate and efficient solutions to differential equations, making them applicable to a wide range of crucial applications in science and engineering.
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    Searches for scalar resonances using diphotons in association with leptons with the ATLAS detector at the LHC
    (University of the Witwatersrand, Johannesburg, 2026) Makgetha, Kutlwano Chongo; Kumar, Mukesh; Mellado, Bruce
    In the first project, we examine recent studies that have identified growing excesses in scalar resonances with diphoton (γγ) final states at the Large Hadron Collider (LHC). These results suggest the presence of scalar particles beyond the Standard Model (BSM), motivated by multi-lepton anomalies observed in Run 2 data from the ATLAS and CMS experiments. Phenomenological studies of the Higgs spectrum side- bands have indicated the presence of a new scalar S with a mass of 152 ± 1 GeV, originating from a heavier scalar H, with a global significance of 5.3σ. ATLAS has launched a new analysis using the easyjet analysis framework with Run 3 data. This analysis searches for scalar resonances produced via γγ channels in association with leptons, inspired by the Two-Higgs Doublet with a Singlet (2HDM+S) and triplet models. Given the complexity of the signal, we carefully account for the dominant background processes, including γγ + multijets, V γγ and t¯tγγ production. The study uses Monte Carlo (MC) samples corresponding to the 2022 ATLAS Run 3 data, with an integrated luminosity of 35.7 fb−1 at √s = 13.6 TeV. The objective is to provide a comprehensive analysis of the significance of this channel, to develop and implement advanced multivariate analysis techniques including deep neural networks, and to con- tribute to the understanding of potential new scalar particles and their implications for physics beyond the SM. In the second project, we build upon the ATLAS and CMS discovery of the Higgs boson (h) decaying into a Z boson and a photon (γ), a rare process within SM that occurs at next-to-leading order (NLO) with a 3.4σ significance. Even with NLO quantum chro- modynamics (QCD) corrections and signal-background interference, SM predictions exceed the observed signal rate by 2.2σ ± 0.7, indicating possible new physics effects or systematic uncertainties that validate further investigation. We investigate this rare process using machine learning (ML) techniques. We use the XGBoost classifier to analyse the production modes of the h → Zγ process, particularly the gluon-gluon fusion (ggF). We use the kernel density estimation (KDE) technique to visualise and iii compare the probability density functions of the classification scores for the generated signal and background events. This provides insights into their separation and overlap within the framework of the Standard Model Effective Field Theory (SMEFT). We aim to constrain the six-dimensional Wilson coefficients and shed light on potential deviations from SM prediction. These methodological innovations demonstrate the effective integration of advanced ML techniques in precision Higgs physics and BSM searches.
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    Genome assembly, annotation and comparative analysis of Cannabis sativa Swazi Gold (SG)
    (University of the Witwatersrand, Johannesburg, 2025) Glen, Tereka; Botes, Angela