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| Paper: |
Single Imaging Atmospheric Cherenkov Telescope Full-Event Reconstruction with a Deep Multi-Task Learning Architecture |
| Volume: |
532, ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS XXX |
| Page: |
203 |
| Authors: |
Jacquemont, M.; Vuillaume, T.; Benoit, A.; Maurin, G.; Lambert, P.; CTA Consortium |
| Abstract: |
The Cherenkov Telescope Array (CTA) is the next generation ground-based observatory for γ-ray astronomy. It will be used to study γ-ray sources, allowing to better understand the Universe. One order of magnitude more sensitive than the current generation of experiments, CTA will propose unseen challenges to standard reconstruction methods.
The GammaLearn project offers to apply deep learning as a part of the analysis of CTA data. Its goal is to separate the γ photons from cosmic particles, and reconstruct the γ photon parameters (energy and arrival direction) from noisy unconventional images, with expected better performance and faster reconstruction than standard methods.
Here we present a complete reconstruction of IACT events using state-of-the-art deep learning techniques. The network is then applied in the single telescope context of the LST1, the first CTA telescope prototype built on the Northern hemisphere site (La Palma, Canary Island). We show that the full event reconstruction is possible with a single multi-task network, reducing the computing needs. |
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