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

Deep unsupervised domain adaptation applied to the Cherenkov Telescope Array Large-Sized Telescope