Deep learning in real-time for the Cherenkov Telescope Array – DIRECTA

DIRECTA (Deep learnIng in REal time for the Cherenkov Telescope Array), as the name states, is a project to apply deep learning solutions based on convolutional neural networks (CNNs) to the Cherenkov Telescope Array (CTA), in real-time. It is a continuation of the GammaLearn project, that already demonstrated the applicability of CNNs to CTA data, and of the ACADA work package that is developing the real-time analysis for CTA using the standard reconstruction techniques. Its objective is the demonstration of the applicability of CNNs in real-time for CTA with a working proof-of-concept applied to the already observing Large-Sized Telescope 1 (LST-1) and later to the LST-2 and Mid-Sized Telescope 1 whose construction will start in 2023. It will greatly improve CTA's reconstruction performances in real-time necessary for the study of transient sources such as gamma-ray bursts and flaring active galactic nuclei, of the Lorentz Invariance Violation and of the Extragalactic Background Light.