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Arrays of imaging atmospheric Cherenkov telescopes (IACT) are superbinstruments to probe the very-high-energy gamma-ray sky. This type of telescopefocuses the Cherenkov light emitted from air showers, initiated byvery-high-energy gamma rays and cosmic rays, onto the camera plane. Then, afast camera digitizes the longitudinal development of the air shower, recordingits spatial, temporal, and calorimetric information. The properties of theprimary very-high-energy particle initiating the air shower can then beinferred from those images: the primary particle can be classified as a gammaray or a cosmic ray and its energy and incoming direction can be estimated.This so-called full-event reconstruction, crucial to the sensitivity of thearray to gamma rays, can be assisted by machine learning techniques. We presenta deep-learning driven, full-event reconstruction applied to simulated IACTevents using CTLearn. CTLearn is a Python package that includes modules forloading and manipulating IACT data and for running deep learning models withTensorFlow, using pixel-wise camera data as input.

Reconstruction of IACT events using deep learning techniques with CTLearn