The use of machine learning to extract a physics model from ATLAS experiment at the LHC
Researcher: Thanyani Gumani, University of Venda
Supervisor: Professor Bruce Mellado, University of the Witwatersrand, Johannesburg
The standard model of particle physics outlines our understanding of the fundamental particles of existence and their interactions. To enhance our understanding of this area, experiments with ever greater energies and intensities have been needed, generating extremely large and detailed data samples. The use of machine learning methods revolutionizes the analysis of these data samples and greatly increases current and future research in their capacity for exploration. There is an overview into the ATLAS experiment and the LHC and Decision Trees and the debate about possible insights and issues. The connections between the machine learning and energy physics analysis are discussed. We consider the supervised machine learning classification in this paper. In this study we apply the MVA methods proposed to analyse their performance using the di-lepton data from the ATLAS experiment at the LHC. Results demonstrate the good performance of the chosen MVA methods, where TMVA is used for computation.