Particle identification in ground-based gamma-ray astronomy using convolutional neural networks
Abstract
Modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air Cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. For example, the camera of the TAIGA-IACT telescope has 560 pixels of hexagonal structure. Images in such cameras can be analysed by deep learning techniques to extract numerous physical and geometrical parameters and/or for incoming particle identification. The most powerful deep learning technique for image analysis, the so-called convolutional neural network (CNN), was implemented in this study. Two open source libraries for machine learning, PyTorch and TensorFlow, were tested as possible software platforms for particle identification in imaging air Cherenkov telescopes. Monte Carlo simulation was performed to analyse images of gamma-rays and background particles (protons) as well as estimate identification accuracy. Further steps of implementation and improvement of this technique are discussed.
Keywords
Cite
@article{arxiv.1812.01551,
title = {Particle identification in ground-based gamma-ray astronomy using convolutional neural networks},
author = {E. B. Postnikov and I. V. Bychkov and J. Y. Dubenskaya and O. L. Fedorov and Y. A. Kazarina and E. E. Korosteleva and A. P. Kryukov and A. A. Mikhailov and M. D. Nguyen and S. P. Polyakov and A. O. Shigarov and D. A. Shipilov and D. P. Zhurov},
journal= {arXiv preprint arXiv:1812.01551},
year = {2018}
}
Comments
5 pages, 2 figures. Submitted to CEUR Workshop Proceedings, 8th International Conference "Distributed Computing and Grid-technologies in Science and Education" GRID 2018, 10 - 14 September 2018, Dubna, Russia