Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data

Journal article


Publication Details

Author(s): Shilon I, Kraus M, Büchele M, Egberts K, Fischer T, Holch TL, Lohse T, Schwanke U, Steppa C, Funk S
Journal: Astroparticle Physics
Publication year: 2019
Volume: 105
Pages range: 44-53
ISSN: 0927-6505


Abstract

Ground based gamma-ray observations with Imaging Atmospheric Cherenkov Telescopes (IACTs) play a significant role in the discovery of very high energy (E > 100 GeV) gamma-ray emitters. The analysis of IACT data demands a highly efficient background rejection technique, as well as methods to accurately determine the position of its source in the sky and the energy of the recorded gamma-ray. We present results for background rejection and signal direction reconstruction from first studies of a novel data analysis scheme for IACT measurements. The new analysis is based on a set of Convolutional Neural Networks (CNNs) applied to images from the four H.E.S.S. phase-I telescopes. As the H.E.S.S. cameras pixels are arranged in a hexagonal array, we demonstrate two ways to use such image data to train CNNs: by resampling the images to a square grid and by applying modified convolution kernels that conserve the hexagonal grid properties.


FAU Authors / FAU Editors

Büchele, Matthias
Lehrstuhl für Physik
Funk, Stefan Prof. Dr.
Lehrstuhl für Physik
Shilon, Idan
Lehrstuhl für Physik


External institutions with authors

Humboldt-Universität zu Berlin
Universität Potsdam


How to cite

APA:
Shilon, I., Kraus, M., Büchele, M., Egberts, K., Fischer, T., Holch, T.L.,... Funk, S. (2019). Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data. Astroparticle Physics, 105, 44-53. https://dx.doi.org/10.1016/j.astropartphys.2018.10.003

MLA:
Shilon, Idan, et al. "Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data." Astroparticle Physics 105 (2019): 44-53.

BibTeX: 

Last updated on 2019-22-02 at 11:23