  The Cherenkov Telescope Array (CTA) is the future ground-based gamma-ray
observatory and will be composed of two arrays of imaging atmospheric Cherenkov
telescopes (IACTs) located in the Northern and Southern hemispheres
respectively. The first CTA prototype telescope built on-site, the Large-Sized
Telescope (LST-1), is under commissioning in La Palma and has already taken
data on numerous known sources. IACTs detect the faint flash of Cherenkov light
indirectly produced after a very energetic gamma-ray photon has interacted with
the atmosphere and generated an atmospheric shower. Reconstruction of the
characteristics of the primary photons is usually done using a parameterization
up to the third order of the light distribution of the images. In order to go
beyond this classical method, new approaches are being developed using
state-of-the-art methods based on convolutional neural networks (CNN) to
reconstruct the properties of each event (incoming direction, energy and
particle type) directly from the telescope images. While promising, these
methods are notoriously difficult to apply to real data due to differences
(such as different levels of night sky background) between Monte Carlo (MC)
data used to train the network and real data. The GammaLearn project, based on
these CNN approaches, has already shown an increase in sensitivity on MC
simulations for LST-1 as well as a lower energy threshold. This work applies
the GammaLearn network to real data acquired by LST-1 and compares the results
to the classical approach that uses random forests trained on extracted image
parameters. The improvements on the background rejection, event direction, and
energy reconstruction are discussed in this contribution.
