Application of Deep Learning methods to analysis of Imaging Atmospheric Cherenkov Telescopes data

Understand

Ground based gamma-ray observations with Imaging Atmospheric Cherenkov Telescopes (IACTs) play a significant role in the discovery of very high energy (E > 100 GeV) gamma-ray emitters.

• The analysis of IACT data demands a highly efficient background rejection technique, as well as methods to accurately determine the energy of the recorded gamma-ray and the position of its source in the sky.
• We present results for background rejection and signal direction reconstruction from first studies of a novel data analysis scheme for IACT measurements.
• The new analysis is based on a set of Convolutional Neural Networks (CNNs) applied to images from the four H.E.S.S.