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

Abstract

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.

References in corpus (6)

• Observations of the Crab Nebula with H.E.S.S
• Deep CORAL: Correlation Alignment for Deep Domain Adaptation
• Gamma-Hadron Separation in Very-High-Energy gamma-ray astronomy using a multivariate analysis method
• A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
• Reconstruction of IACT events using deep learning techniques with CTLearn
• IACT event analysis with the MAGIC telescopes using deep convolutional neural networks with CTLearn