Indirect Dark Matter Searches In The Gamma Ray Band And Development Of New Analysis Techniques For Ground Based Gamma Ray Astronomy

Arrays of imaging atmospheric Cherenkov telescopes (IACTs) probe the very highenergy (VHE) gamma-ray sky. Their working principle consists of the simultaneous observation of air showers initiated by the interaction of VHE gamma rays and cosmic rays with the atmosphere. Cherenkov photons induced by a given shower are focused onto the camera plane of the telescopes in the array, producing a stereoscopic record of the event. This image contains the longitudinal development of the airshower, together with its spatial, temporal, and calorimetric information. The properties of the originating VHE particle (type, energy, and incoming direction) can be inferred from those images by reconstructing the whole event using machine learning techniques. In this thesis, a purely deep learning (DL) driven full-event reconstruction of simulated, stereoscopic IACT events of the future Cherenkov Telescope Array (CTA) is presented. In addition, we apply DL algorithms on real observational IACT data, utilizing Crab Nebula observations by the MAGIC telescopes. In order to conduct all necessary research to achieve the former milestones we developed CTLearn, a package that includes modules for loading and manipulating IACT data and for running DL models, using pixel-wise camera data as input...