The performance of the MAGIC telescopes using deep convolutional neural networks with CTLearn by T. Miener et al. on Wednesday 30 November

The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescope system is

located on the Canary Island of La Palma and inspects the very high-energy

(VHE, few tens of GeV and above) gamma-ray sky. MAGIC consists of two imaging

atmospheric Cherenkov telescopes (IACTs), which capture images of the air

showers originating from the absorption of gamma rays and cosmic rays by the

atmosphere, through the detection of Cherenkov photons emitted in the shower.

The sensitivity of IACTs to gamma-ray sources is mainly determined by the

ability to reconstruct the properties (type, energy, and arrival direction) of

the primary particle generating the air shower. The state-of-the-art IACT

pipeline for shower reconstruction is based on the parameterization of the

shower images by extracting geometric and stereoscopic features and machine

learning algorithms like random forest or boosted decision trees. In this

contribution, we explore deep convolutional neural networks applied directly to

the pixelized images of the camera as a promising method for IACT full-event

reconstruction and present the performance of the method on observational data

using CTLearn, a package for IACT event reconstruction that exploits deep

learning.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.16009v1