The Cherenkov Telescope Array is the next generation of observatory using
imaging air Cherenkov technique for very-high-energy gamma-ray astronomy. Its
first prototype telescope is operational on-site at La Palma and its data
acquisitions allowed to detect known sources, study new ones, and to confirm
the performance expectations. The application of deep learning for the
reconstruction of the incident particle physical properties (energy, direction
of arrival and type) have shown promising results when conducted on
simulations. Nevertheless, its application to real observational data is
challenging because deep-learning-based models can suffer from domain shifts.
In the present article, we address this issue by implementing domain adaptation
methods into state-of-art deep learning models for Imaging Atmospheric
Cherenkov Telescopes event reconstruction to reduce the domain discrepancies,
and we shed light on the gain in performance that they bring along