  The Cherenkov Telescope Array (CTA), conceived as an array of tens of imaging
atmospheric Cherenkov telescopes (IACTs), is an international project for a
next-generation ground-based gamma-ray observatory, aiming to improve on the
sensitivity of current-generation instruments a factor of five to ten and
provide energy coverage from 20 GeV to more than 300 TeV. Arrays of IACTs probe
the very-high-energy gamma-ray sky. Their working principle consists of the
simultaneous observation of air showers initiated by the interaction of
very-high-energy 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 multi-stereoscopic record of the event.
This image contains the longitudinal development of the air shower, together
with its spatial, temporal, and calorimetric information. The properties of the
originating very-high-energy particle (type, energy, and incoming direction)
can be inferred from those images by reconstructing the full event using
machine learning techniques. In this contribution, we present a purely
deep-learning driven, full-event reconstruction of simulated, stereoscopic IACT
events using CTLearn. CTLearn is a package that includes modules for loading
and manipulating IACT data and for running deep learning models, using
pixel-wise camera data as input.
