Selection of Gamma Events from IACT Images Using Deep Learning Methods

Imaging atmospheric cherenkov telescopes (IACTs) of the 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 rays 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 the image classification task on Monte Carlo (MC) images of the TAIGA-IACTs are presented. The wobbling mode is considered together with the image adaptation for the 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.