Deep Unsupervised Domain Adaptation Applied to the Cherenkov Telescope Array Large-Sized Telescope
Abstract
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.
Fields of Study & Subjects
Astrophysics and Cosmic Phenomena
Gamma-ray bursts and supernovae
Radiation Detection and Scintillator Technologies
Publication details
- Journal / source
- Astronomical Society of the Pacific Conference Series
- Publisher
- Astronomical Society of the Pacific Conference Series
- Volume
- 541
- Pages
- 210–213
- Publication date
- 28 February 2026
- Language
- EN