Development of new analysis techniques for ground-based gamma-ray astronomy (Tjark Miener – Univesidad Complutense de Madrid)

Imaging atmospheric Cherenkov telescopes (IACTs) are excellent tools to inspect the very high-energy (few tens of GeV and above) gamma-ray sky by capturing images of the air showers, originated by the absorption of gamma rays and cosmic rays by the atmosphere, through the detection of Cherenkov photons emitted in the shower. One of the main factors determining the sensitivity of IACTs to gamma-ray sources, in general, is how well reconstructed the properties (type, energy, and incoming direction) of the primary particle triggering the air shower are. We present how deep convolutional neural networks (CNNs) are being explored as a promising method for IACT full-event reconstruction. The performance of the method is evaluated on simulated data from the future Cherenkov Telescope Array (CTA) and on observational data from the current-generation Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescope system using CTLearn, a package for IACT event reconstruction through deep learning.