Selection of gamma events from IACT images with deep learning methods
This work addresses a domain-specific challenge in gamma ray astronomy by enhancing event selection accuracy for IACT observations, though it appears incremental as it builds on existing neural network methods with adaptations for wobbling mode.
The study tackled the problem of classifying gamma ray events from background cosmic rays in Imaging Atmospheric Cherenkov Telescopes (IACTs) under wobbling observation mode, which distorts images and affects neural network performance, by applying neural networks to Monte Carlo images and achieving improved segregation quality with specific metrics evaluated.
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.