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Department of Physics
Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN
Abstract
dc:description.abstractThis 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).
Degree
thesis:*- Grantor
- Department of Physics
- Year dc:date.issued
- 2019
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Viljoen, Christiaan Gerhardus
- Advisor dc:contributor.advisor
-
- Dietel, Thomas
Subjects
dc:subject × 1Identifiers
dc:identifier.*- Handle dc:identifier.uri
- https://hdl.handle.net/11427/31781
- OAI identifier oai:identifier
- oai:open.uct.ac.za:11427/31781