Event classification for gamma-hadron separation for H.E.S.S
Researcher: WandileLesejane, University of the Witwatersrand, Johannesburg
Supervisor: Prof Nukri Komin, University of the Witwatersrand, Johannesburg
The H.E.S.S is one of the best IACTs and is crucial for studying cosmic particles, particularly gamma induced particles. It is able todetect particles with energies ranging from tens of GeV to TeV. The challenge stems from the influx of the hadronic air showers which are more common and can obscure the detection of gamma particles. Deep Neural Networks were employed to discriminate the gamma events from hadron events using data that was simulated using KASKADE and SMASH softwares. The model had a performance accuracy of 97.42% and a loss of 7.21% at its best and an accuracy of 53% at its poorest.