Leveraging Machine Learning in the Search for New Bosons at the LHC and Other Resulting Applications
| dc.contributor.author | Stevenson, Finn David | |
| dc.contributor.supervisor | Mellado, Bruce | |
| dc.date.accessioned | 2024-10-15T08:08:27Z | |
| dc.date.available | 2024-10-15T08:08:27Z | |
| dc.date.issued | 2023-09 | |
| dc.description | A dissertation submitted in fulfillment of the requirements for the degree of Master of Science (Wits Institute of Collider Particle Physics), Faculty of Science, School of Physics, University of the Witwatersrand, Johannesburg, 2023. | |
| dc.description.abstract | This dissertation focuses on the use of semi-supervised machine learning for data generation in high-energy physics, specifically to aid in the search for new bosons at the Large Hadron Collider. The overarching physics analysis for this work involves the development of a generative machine learning model to assist in the search for resonances in the Zγ final state background data. A number of Variational Auto-encoder (VAE) derivatives are developed and trained to be able to generate a chosen Monte Carlo fast simulated dataset. These VAE derivatives are then evaluated using chosen metrics and plots to assess their performance in data generation. Overall, this work aims to demonstrate the utility of semi-supervised machine learning techniques in the search for new resonances in high-energy physics. Additionally, a resulting application of the use of machine learning in COVID-19 crisis management was also documented. | |
| dc.description.sponsorship | Africa-Canada Artificial Intelligence Data Modelling Consortium. | |
| dc.description.sponsorship | National Research Foundation (NRF) | |
| dc.description.sponsorship | SA-CERN | |
| dc.description.submitter | MM2024 | |
| dc.faculty | Faculty of Science | |
| dc.identifier | 0000-0003-0444-2992 | |
| dc.identifier.citation | Stevenson, Finn David. (2023). Leveraging Machine Learning in the Search for New Bosons at the LHC and Other Resulting Applications. [Master's dissertation, University of the Witwatersrand, Johannesburg]. https://hdl.handle.net/10539/41583 | |
| dc.identifier.uri | https://hdl.handle.net/10539/41583 | |
| dc.language.iso | en | |
| dc.publisher | University of the Witwatersrand, Johannesburg | |
| dc.rights | ©2023 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg. | |
| dc.rights.holder | University of the Witwatersrand, Johannesburg | |
| dc.school | School of Physics | |
| dc.subject | Machine learning | |
| dc.subject | High energy physics | |
| dc.subject | Deep learning | |
| dc.subject | Data generation | |
| dc.subject | UCTD | |
| dc.subject.other | SDG-9: Industry, innovation and infrastructure | |
| dc.title | Leveraging Machine Learning in the Search for New Bosons at the LHC and Other Resulting Applications | |
| dc.type | Dissertation |