# Analysis of the HiSCORE Simulated Events in TAIGA Experiment Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2112.10170 · HTML: https://mlsanity.com/paper/2112.10170/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 19 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Deep Learning for Energy Estimation and Particle Identification in Gamma-ray Astronomy](https://mlsanity.com/paper/1907.10480/) (2019)

## Abstract (arXiv)

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.