Speaker
Description
The High Energy Stereoscopic System (H.E.S.S.) plays an important role in detecting
secondary particles generated by primary particle collisions with atmospheric nuclei, leading to Extensive Air Showers (EAS). Distinguishing between gamma-ray and hadron-induced showers is crucial for maximizing the sensitivity of the instrument. However, this task is challenging due to the similarity in their detector signatures. Recent studies using the large H.E.S.S. CT5 telescope as a muon tagger for background rejection have allowed for the recent detection of
the microquasar SS433 by H.E.S.S. This research aims to enhance this approach by identifying cosmics ray muons in waveform data from the Flashcam CT5 telescope camera. Techniques such as change point detection and peak finding algorithms are explored. Unfortunately we find that this approach does not lead to a significant enhancement in the background rejection power. However, more sophisticated deep learning techniques could be applied to this data which might present superior results.
