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.