Electronic Theses and Dissertations (PhDs)

Permanent URI for this collectionhttps://hdl.handle.net/10539/38021

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Start date: 2020Subject: search.filters.subject.ATLAS

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    Development and Reliability Testing of a new Low-Voltage Power Supply for the ATLAS Hadronic Tile-Calorimeter Phase-II Upgrade
    (University of the Witwatersrand, Johannesburg, 2024-06) Mckenzie, Ryan Peter; Solans, Carlos; Mellado, Bruce
    The Large Hadron Collider (LHC), located at the Conseil Européan pour la Recherche Nucléaire (CERN) also known as the European Laboratory of Particle Physics, is a tworing-superconducting-hadron accelerator and collider located on the Franco-Swiss border. The LHC was successfully commissioned in 2010 for proton–proton collisions and is expected to deliver 500 f b−1 before Long Shutdown three (LS3) that is schedule to commence in 2026. Its successor, the HL-LHC, will provide a levelled instantaneous luminosity of L = 5x 1034 cm−2 s−1 and is projected to deliver an integrated luminosity of more than 4000 fb−1 to its two general purpose detectors, known as A Toroidal LHC Apparatus (ATLAS) and Compact Muon Solenoid (CMS), over a span of 10 years. The main motivation to upgrade the LHC is to fully exploit its physics potential. Through a series of machine and detector upgrades, it is possible to increase the instantaneous luminosity. This could unlock many of the physics processes that are today inaccessible to the LHC because of the lack of statistics. The primary impacts of the HL-LHC on the detector environment are a direct consequence of an increase in delivered instantaneous luminosity. The ATLAS experiment will undergo its Phase-II Upgrade during Long-Shutdown 3 to ensure peak performance during high-luminosity operations. ATLAS is composed of several specialized sub-detectors one of which is the hadronic Tile-Calorimeter (TileCal). The TileCal will undergo numerous upgrades on of which will be to the Low-Voltage (LV) power distribution system that services its on-detector electronics. The on-detector finger Low-Voltage Power supplies form the second stage of the LV system. Their primary functional device is a transformer-coupled buck converter, known as a Brick, which is responsible for converting bulk power to that required by the on-detector electronics. All legacy Bricks will be replaced with a new version that employs several design changes to enable their reliable operation within the HL-LHC detector environment. In this thesis, the development of the Phase-II Upgrade Brick is presented with an emphasis placed on its thermal performance and reliability. A thermal analysis of the proposed upgrade Brick versions is presented with design changes occurring as a result. Due to the design change incorporating a new active component an irradiation campaign is conducted to qualify it for use within the high-luminosity detector environment. A reliability analysis of the Phase-II upgrade Brick is conducted necessitated by the change of many critical components. The quality assurance procedure of the Bricks that is undertaken post-production is presented with particular attention placed on their Burn-in testing.
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    The Use of Semi-Supervised Machine Learning Techniques in the Search for New Bosons with the ATLAS Detector
    (University of the Witwatersrand, Johannesburg, 2024-06) Lieberman, Benjamin; Mellado, Bruce
    Since the completion of the Standard Model, with the discovery of the Higgs Boson, there has been a significant shift in the exploration of new physics to explain deviations between model simulated data and that produced at the Large Hadron Collider. These investigations are greatly aided by the integration of advanced machine learning techniques. Machine learning methods offer powerful solutions for complex collider physics challenges. However, these models often depend on simulations that might not fully align with actual physical phenomena. In order to remove this model dependency, semi-supervised classifiers can be used. This solution, however, is not without challenges. In this thesis, an evaluation of the use and limitations of semi-supervised classifiers in particle physics is presented. This is achieved by using a well constrained di-lepton final state dataset to assess the efficacy and ability of the technique, compared to its supervised counterpart. Following this baseline study, the S(150) ! Zg final state, motivated by the multi-lepton anomalies at the LHC, is used to perform narrow resonance searches with semi-supervised Neural Network (NN) classifiers. This work details the methodology and outcomes of a frequentist study aimed at quantifying the extent of a trials factor introduced by semi-supervised NN classifiers. This involves an in-depth analysis of the rate of statistical fluctuations generated in the training of semi-supervised techniques. A secondary component of this study is the evaluation of machine learning based data generators, with emphasis on the Wasserstein Generative Adversarial Network (WGAN), to produce large quantities of realistic data for physics analyses. The results of this investigation into semi-supervised techniques, firstly validates the efficacy and ability of these techniques to classify particle events. This is followed by the frequentist study results, which substantiation that the trials factor remains suitably managed with the use of semi-supervised NN classifiers. The insights derived from this research pave the way for enhancing the reliability of upcoming resonance searches, underscoring the potential of semi-supervised learning in searches for narrow resonances.