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The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescope systemconsists of two imaging atmospheric Cherenkov telescopes (IACTs) and is locatedon the Canary island of La Palma. IACTs are excellent tools to inspect thevery-high-energy (few tens of GeV and above) gamma-ray sky by capturing imagesof the air showers, originated by the absorption of gamma rays and cosmic raysby the atmosphere, through the detection of Cherenkov photons emitted in theshower. One of the main factors determining the sensitivity of IACTs togamma-ray sources, in general, is how well reconstructed the properties (type,energy, and incoming direction) of the primary particle triggering the airshower are. We present how deep convolutional neural networks (CNNs) are beingexplored as a promising method for IACT full-event reconstruction. Theperformance of the method is evaluated on observational data using the standardMAGIC Analysis and Reconstruction Software, MARS, and CTLearn, a package forIACT event reconstruction through deep learning.

IACT event analysis with the MAGIC telescopes using deep convolutional neural networks with CTLearn