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Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN
This Masters thesis outlines the application of machine learning techniques, predominantly deep learning techniques, towards certain aspects of particle physics. Its two main aims: particle identification and high energy physics detector simulations are pertinent to research avenues pursued by physi...
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| Format: | Thesis |
| Language: | English |
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Department of Physics
2020
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| _version_ | 1867613245470146560 |
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| access_status_str | Open Access |
| author | Viljoen, Christiaan Gerhardus |
| author2 | Dietel, Thomas |
| author_browse | Dietel, Thomas Viljoen, Christiaan Gerhardus |
| author_facet | Dietel, Thomas Viljoen, Christiaan Gerhardus |
| author_sort | Viljoen, Christiaan Gerhardus |
| collection | Thesis |
| description | This Masters thesis outlines the application of machine learning techniques, predominantly deep learning techniques, towards certain aspects of particle physics. Its two main aims: particle identification and high energy physics detector simulations are pertinent to research avenues pursued by physicists working with the ALICE (A Large Ion Collider Experiment) Transition Radiation Detector (TRD), within the Large Hadron Collider (LHC) at CERN (The European Organization for Nuclear Research). |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/31781 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:05.164Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Department of Physics |
| publisherStr | Department of Physics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/31781 Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN Viljoen, Christiaan Gerhardus Dietel, Thomas Physics This Masters thesis outlines the application of machine learning techniques, predominantly deep learning techniques, towards certain aspects of particle physics. Its two main aims: particle identification and high energy physics detector simulations are pertinent to research avenues pursued by physicists working with the ALICE (A Large Ion Collider Experiment) Transition Radiation Detector (TRD), within the Large Hadron Collider (LHC) at CERN (The European Organization for Nuclear Research). 2020-05-06T02:23:15Z 2020-05-06T02:23:15Z 2019 2020-05-06T01:48:48Z Master Thesis Masters MSc https://hdl.handle.net/11427/31781 eng application/pdf Department of Physics Faculty of Science |
| spellingShingle | Physics Viljoen, Christiaan Gerhardus Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN |
| thesis_degree_str | Master's |
| title | Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN |
| title_full | Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN |
| title_fullStr | Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN |
| title_full_unstemmed | Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN |
| title_short | Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN |
| title_sort | machine learning for particle identification amp deep generative models towards fast simulations for the alice transition radiation detector at cern |
| topic | Physics |
| url | https://hdl.handle.net/11427/31781 |
| work_keys_str_mv | AT viljoenchristiaangerhardus machinelearningforparticleidentificationampdeepgenerativemodelstowardsfastsimulationsforthealicetransitionradiationdetectoratcern |