Applying Machine Learning to Gamma-Hadron Separation in Gamma-Ray Astronomy

Written by NEPTTP on November 25, 2020. Posted in Capstone Research Projects

Researcher: Johannah Moepi, University of the Witwatersrand, Johannesburg
Supervisor: Professor Nukri Komin, University of the Witwatersrand, Johannesburg

With two types of EAS produced, the IACT’s detect both of them including the background events.
As such the IACT’s requires sophisticated image analysis that can be trained to distinguish gamma rays from the hadrons.