Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data
Shilon I, Kraus M, Büchele M, Egberts K, Fischer T, Holch TL, Lohse T, Schwanke U, Steppa C, Funk S (2019)
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 105
Pages Range: 44-53
DOI: 10.1016/j.astropartphys.2018.10.003
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.
Authors with CRIS profile
Idan Shilon
Lehrstuhl für Physik
Matthias Büchele
Lehrstuhl für Physik
Stefan Funk
Lehrstuhl für Physik
Involved external institutions
Germany (DE)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.
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