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Abstract

The spectrum of cosmic-ray electrons has so far been measured using balloon and satellitebased instruments. At TeV energies, however, the sensitivity of such instruments is very limited due to the low flux of electrons at very high energies and small detection areas of balloon/satellite based experiments. The very large collection area of ground-based imaging atmospheric Cherenkov telescopes gives them a substantial advantage over balloon/ satellite based instruments when detecting very-high-energy electrons (> 300 GeV). By analysing data taken by the High Energy Stereoscopic System (H.E.S.S.), this work extends the known electron spectrum up to 4 TeV a range that is not accessible to direct measurements. However, in contrast to direct measurements, imaging atmospheric Cherenkov telescopes such as H.E.S.S. detect air showers that comic-ray electrons initiate in the atmosphere rather than the primary particle. Thus, the main challenge is to differentiate between air showers initiated by electrons and those initiated by the hadronic background. A new analysis technique was developed that determines the background with the support of the machine-learning algorithm Random Forest. It is shown that this analysis technique can also be applied in other areas such as the analysis of diffuse rays from the Galactic plane.