Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN

 

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dc.contributor.advisor Dietel, Thomas
dc.contributor.author Viljoen, Christiaan Gerhardus
dc.date.accessioned 2020-05-06T02:23:15Z
dc.date.available 2020-05-06T02:23:15Z
dc.date.issued 2019
dc.identifier.citation Viljoen, C.G. 2019. Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN. . ,Faculty of Science ,Department of Physics. en_ZA
dc.identifier.uri https://hdl.handle.net/11427/31781
dc.description.abstract 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).
dc.subject Physics
dc.title Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN
dc.type Master Thesis
dc.date.updated 2020-05-06T01:48:48Z
dc.language.rfc3066 eng
dc.publisher.faculty Faculty of Science
dc.publisher.department Department of Physics
dc.type.qualificationlevel Masters
dc.type.qualificationname MSc
dc.identifier.apacitation Viljoen, C. G. (2019). <i>Machine learning for particle identification &amp; deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN</i>. (). ,Faculty of Science ,Department of Physics. Retrieved from en_ZA
dc.identifier.chicagocitation Viljoen, Christiaan Gerhardus. <i>"Machine learning for particle identification &amp; deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN."</i> ., ,Faculty of Science ,Department of Physics, 2019. en_ZA
dc.identifier.vancouvercitation Viljoen CG. Machine learning for particle identification &amp; deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN. []. ,Faculty of Science ,Department of Physics, 2019 [cited yyyy month dd]. Available from: en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Viljoen, Christiaan Gerhardus AB - 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). DA - 2019 DB - OpenUCT DP - University of Cape Town KW - Physics LK - https://open.uct.ac.za PY - 2019 T1 - Machine learning for particle identification &amp; deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN TI - Machine learning for particle identification &amp; deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN UR - ER - en_ZA


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