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

TL;DR

This paper explores the use of deep unsupervised domain adaptation techniques to improve the accuracy of event reconstruction in the Cherenkov Telescope Array, addressing the domain shift challenge between simulated and real observational data.

Contribution

It introduces domain adaptation methods into deep learning models for Cherenkov Telescope data, enhancing their performance on real observational data.

Findings

Domain adaptation reduces discrepancies between simulated and real data.

Improved event reconstruction accuracy with domain adaptation.

Enhanced robustness of deep learning models in gamma-ray astronomy.

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.