Processing Images from Multiple IACTs in the TAIGA Experiment with Convolutional Neural Networks
This work addresses event classification and energy estimation for gamma-ray astronomy in the TAIGA experiment, representing an incremental application of existing CNNs to new telescope data.
The paper tackled the problem of distinguishing gamma-ray events from hadrons and estimating gamma-ray energy using convolutional neural networks (CNNs) on simulated images from the TAIGA experiment's IACTs, finding that CNNs using images from two telescopes performed better than those using a single telescope.
Extensive air showers created by high-energy particles interacting with the Earth atmosphere can be detected using imaging atmospheric Cherenkov telescopes (IACTs). The IACT images can be analyzed to distinguish between the events caused by gamma rays and by hadrons and to infer the parameters of the event such as the energy of the primary particle. We use convolutional neural networks (CNNs) to analyze Monte Carlo-simulated images from the telescopes of the TAIGA experiment. The analysis includes selection of the images corresponding to the showers caused by gamma rays and estimating the energy of the gamma rays. We compare performance of the CNNs using images from a single telescope and the CNNs using images from two telescopes as inputs.