GammaLearn - first steps to apply Deep Learning to the Cherenkov Telescope Array data

Thomas Vuillaume (1) , Mikaël Jacquemont (1, 2) , Luca Antiga , A Benoit (2) , Patrick Lambert (2) , Gilles Maurin (3, 1) , Giorgia Silvestri

1 LAPP - Laboratoire d'Annecy de Physique des Particules
2 LISTIC - Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance
3 APC (UMR_7164) - AstroParticule et Cosmologie

The Cherenkov Telescope Array (CTA) is the next generation of ground-based gamma-ray telescopes for gamma-ray astronomy. Two arrays will be deployed composed of 19 telescopes in the Northern hemisphere and 99 telescopes in the Southern hemisphere. Due to its very high sensitivity, CTA will record a colossal amount of data that represent a computing challenge to the reconstruction software. Moreover, the vast majority of triggered events come from protons that represent a background for gamma-ray astronomy. Deep learning developments in the last few years have shown tremendous improvements in the analysis of data in many domains. Thanks to the huge amount of simulated data and later of real data, produced by CTA, these algorithms look particularly adapted and very promising. Moreover, the trained neural networks show very good computing performances during execution. Here we present a first study of deep learning architectures applied to CTA simulated data to perform the reconstruction of the particles energy and incoming direction and the development of a specific framework, GammaLearn, to accomplish this task.

Domaines: Phénomènes cosmiques de haute energie [astro-ph.HE]; Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Thomas Vuillaume, Mikaël Jacquemont, Luca Antiga, A Benoit, Patrick Lambert, et al.. GammaLearn - first steps to apply Deep Learning to the Cherenkov Telescope Array data. 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018), Jul 2018, Sofia, Bulgaria. ⟨hal-02197396⟩