Energy Reconstruction in Analysis of Cherenkov Telescopes Images in TAIGA Experiment Using Deep Learning Methods. (arXiv:2211.08971v1 [astro-ph.IM])

Imaging Atmospheric Cherenkov Telescopes (IACT) of TAIGA astrophysical
complex allow to observe high energy gamma radiation helping to study many
astrophysical objects and processes. TAIGA-IACT enables us to select gamma
quanta from the total cosmic radiation flux and recover their primary
parameters, such as energy and direction of arrival. The traditional method of
processing the resulting images is an image parameterization - so-called the
Hillas parameters method. At the present time Machine Learning methods, in
particular Deep Learning methods have …

analysis arxiv astro deep learning energy experiment images telescopes