  The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescope system is
located on the Canary Island of La Palma and inspects the very high-energy
(VHE, few tens of GeV and above) gamma-ray sky. MAGIC consists of two imaging
atmospheric Cherenkov telescopes (IACTs), which capture images of the air
showers originating from the absorption of gamma rays and cosmic rays by the
atmosphere, through the detection of Cherenkov photons emitted in the shower.
The sensitivity of IACTs to gamma-ray sources is mainly determined by the
ability to reconstruct the properties (type, energy, and arrival direction) of
the primary particle generating the air shower. The state-of-the-art IACT
pipeline for shower reconstruction is based on the parameterization of the
shower images by extracting geometric and stereoscopic features and machine
learning algorithms like random forest or boosted decision trees. In this
contribution, we explore deep convolutional neural networks applied directly to
the pixelized images of the camera as a promising method for IACT full-event
reconstruction and present the performance of the method on observational data
using CTLearn, a package for IACT event reconstruction that exploits deep
learning.
