Deep Learning for Energy Estimation and Particle Identification in Gamma-ray Astronomy

Abstract

Deep learning techniques, namely convolutional neural networks (CNN), have previously been adapted to select gamma-ray events in the TAIGA experiment, having achieved a good quality of selection as compared with the conventional Hillas approach. Another important task for the TAIGA data analysis was also solved with CNN: gamma-ray energy estimation showed some improvement in comparison with the conventional method based on the Hillas analysis. Furthermore, our software was completely redeveloped for the graphics processing unit (GPU), which led to significantly faster calculations in both of these tasks. All the results have been obtained with the simulated data of TAIGA Monte Carlo software; their experimental confirmation is envisaged for the near future.

arXiv comment10 pages, 6 figures. arXiv admin note: text overlap with arXiv:1812.01551

References in corpus (3)

• Application of Deep Learning methods to analysis of Imaging Atmospheric Cherenkov Telescopes data
• Improved energy resolution for VHE gamma-ray astronomy with systems of Cherenkov telescopes
• ANN-based energy reconstruction procedure for TACTIC gamma-ray telescope and its comparison with other conventional methods

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• The Preliminary Results on Analysis of TAIGA-IACT Images Using Convolutional Neural Networks
• Selection of gamma events from IACT images with deep learning methods
• Processing Images from Multiple IACTs in the TAIGA Experiment with Convolutional Neural Networks
• Analysis of the HiSCORE Simulated Events in TAIGA Experiment Using Convolutional Neural Networks