  The Cherenkov Telescope Array Observatory (CTAO) is the next generation of
observatories employing the imaging air Cherenkov technique for the study of
very high energy gamma rays. The deployment of deep learning methods for the
reconstruction of physical attributes of incident particles has evinced
promising outcomes when conducted on simulations. However, the transition of
this approach to observational data is accompanied by challenges, as deep
learning-based models are susceptible to domain shifts. In this paper, we
integrate domain adaptation in the physics-based context of the CTAO and shed
light on the gain in performance that these techniques bring using LST-1 real
acquisitions.
