Selection of gamma events from IACT images with deep learning methods
E. O. Gres A. P. Kryukov A. P. Demichev J. J. Dubenskaya S. P. Polyakov A. A. Vlaskina D. P. Zhurov
Imaging Atmospheric Cherenkov Telescopes (IACTs) of gamma ray observatory
TAIGA detect the Extesnive Air Showers (EASs) originating from the cosmic or
gamma rays interactions with the atmosphere. Thereby, telescopes obtain images
of the EASs. The ability to segregate gamma rays images from the hadronic
cosmic ray background is one of the main features of this type of detectors.
However, in actual IACT observations simultaneous observation of the background
and the source of gamma ray is needed. This observation mode (called wobbling)
modifies images of events, which affects the quality of selection by neural
networks.
  Thus, in this work, the results of the application of neural networks (NN)
for image classification task on Monte Carlo (MC) images of TAIGA-IACTs are
presented. The wobbling mode is considered together with the image adaptation
for adequate analysis by NNs. Simultaneously, we explore several neural network
structures that classify events both directly from images or through Hillas
parameters extracted from images. In addition, by employing NNs, MC simulation
data are used to evaluate the quality of the segregation of rare gamma events
with the account of all necessary image modifications.