Analysis of the HiSCORE Simulated Events in TAIGA Experiment Using Convolutional Neural Networks
This work addresses the challenge of improving data analysis for high-energy gamma-ray astronomy, but it appears incremental as it applies an existing method (CNN) to a specific dataset without claiming major breakthroughs.
The researchers tackled the problem of determining air shower characteristics like direction, axis position, and energy in gamma-ray astronomy using TAIGA-HiSCORE data by applying convolutional neural networks to treat events as images, and they presented preliminary results comparing this approach to traditional methods.
TAIGA is a hybrid observatory for gamma-ray astronomy at high energies in range from 10 TeV to several EeV. It consists of instruments such as TAIGA-IACT, TAIGA-HiSCORE, and others. TAIGA-HiSCORE, in particular, is an array of wide-angle timing Cherenkov light stations. TAIGA-HiSCORE data enable to reconstruct air shower characteristics, such as air shower energy, arrival direction, and axis coordinates. In this report, we propose to consider the use of convolution neural networks in task of air shower characteristics determination. We use Convolutional Neural Networks (CNN) to analyze HiSCORE events, treating them like images. For this, the times and amplitudes of events recorded at HiSCORE stations are used. The work discusses a simple convolutional neural network and its training. In addition, we present some preliminary results on the determination of the parameters of air showers such as the direction and position of the shower axis and the energy of the primary particle and compare them with the results obtained by the traditional method.