  Arrays of imaging atmospheric Cherenkov telescopes (IACT) are superb
instruments to probe the very-high-energy gamma-ray sky. This type of telescope
focuses the Cherenkov light emitted from air showers, initiated by
very-high-energy gamma rays and cosmic rays, onto the camera plane. Then, a
fast camera digitizes the longitudinal development of the air shower, recording
its spatial, temporal, and calorimetric information. The properties of the
primary very-high-energy particle initiating the air shower can then be
inferred from those images: the primary particle can be classified as a gamma
ray or a cosmic ray and its energy and incoming direction can be estimated.
This so-called full-event reconstruction, crucial to the sensitivity of the
array to gamma rays, can be assisted by machine learning techniques. We present
a deep-learning driven, full-event reconstruction applied to simulated IACT
events using CTLearn. CTLearn is a Python package that includes modules for
loading and manipulating IACT data and for running deep learning models with
TensorFlow, using pixel-wise camera data as input.
