Researcher: Theodore Cwere Gaelejwe, University of the Witwatersrand, Johannesburg
Supervisor: Prof. Bruce Mellado
The Large Hadron Collider (LHC) generates petabytes of data per second during each data taking period and has long term data storage in the order of exabytes. Sophisticated machine learning (ML) techniques are used at the trigger and final state level to analyse this data. Boosted Decision Trees (BDTs) in particular, have been the default ML tool for this task. However, in the recent past, more modern techniques such as Deep Learning have emerged and there has been growing justification for their use in High Energy Physics (HEP). We conduct a comparative study between BDTs and (Deep Neural Networks) DNNs in classifying signal and background events in the H → γγ + Χ decay channel. A comparison between a fully supervised and weakly supervised model is also conducted. Results suggest that DNNs outperform BDTs and the fully supervised model is outperformed by the weakly supervised model though it is more robust.
