Deep Learning & Its Application To Searches Beyond The Standard Model With The ATLAS Detector At The Large Hadron Collider

Researcher: Asiphe Mzaza, University of the Witwatersrand, Johannesburg
Supervisors: Prof. Bruce Mellado, University of the Witwatersrand, Johannesburg

Anomalies in the observations of Run 1 and Run 2 data from the ATLAS detector at the LHC suggest physics beyond the Standard Model. The Madala Hypothesis, attempts to explain these anomalies with new hypothetical scalars H and S with 2mh<mH<2mH and mh<mS<mH, where mh and mt are the masses of the Higgs boson and top-quark. These bosons are being searched in the leptonic decay channels, such as H→SS→4W [1], which have extremely low signal-to-background ratios. A discriminative machine learning algorithm is developed to distinguish between signal and background. Specifically, the viability of a deep neural network is assessed. A neural network model with useful diagnostic power is constructed using simulations of the di-lepton decay process.