  When very-high-energy gamma rays interact high in the Earth's atmosphere,
they produce cascades of particles that induce flashes of Cherenkov light.
Imaging Atmospheric Cherenkov Telescopes (IACTs) detect these flashes and
convert them into shower images that can be analyzed to extract the properties
of the primary gamma ray. The dominant background for IACTs is comprised of air
shower images produced by cosmic hadrons, with typical noise-to-signal ratios
of several orders of magnitude. The standard technique adopted to differentiate
between images initiated by gamma rays and those initiated by hadrons is based
on classical machine learning algorithms, such as Random Forests, that operate
on a set of handcrafted parameters extracted from the images. Likewise, the
inference of the energy and the arrival direction of the primary gamma ray is
performed using those parameters. State-of-the-art deep learning techniques
based on convolutional neural networks (CNNs) have the potential to enhance the
event reconstruction performance, since they are able to autonomously extract
features from raw images, exploiting the pixel-wise information washed out
during the parametrization process. Here we present the results obtained by
applying deep learning techniques to the reconstruction of Monte Carlo
simulated events from a single, next-generation IACT, the Large-Sized Telescope
(LST) of the Cherenkov Telescope Array (CTA). We use CNNs to separate the
gamma-ray-induced events from hadronic events and to reconstruct the properties
of the former, comparing their performance to the standard reconstruction
technique. Three independent implementations of CNN-based event reconstruction
models have been utilized in this work, producing consistent results.
