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Communication Dans Un Congrès Année : 2023

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

Résumé

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.

Dates et versions

hal-04190277 , version 1 (29-08-2023)

Identifiants

Citer

Michaël Dell'Aiera, Thomas Vuillaume, Mikaël Jacquemont, Alexandre Benoit. Deep unsupervised domain adaptation applied to the Cherenkov Telescope Array Large-Sized Telescope. 20th International Conference on Content-based Multimedia Indexing (CBMI 2023), Sep 2023, Orleans, France. pp.133-139, ⟨10.1145/3617233.3617279⟩. ⟨hal-04190277⟩
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