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 Status: Published

Publication Type: Journal article

Publication year: 2019

Journal

Astroparticle Physics Elsevier

Publisher: ELSEVIER SCIENCE BV

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.The networks were trained on sets of Monte-Carlo simulated events and tested on both simulations and measured data from the H.E.S.S. array. A comparison between the CNN analysis to current state-of-the-art algorithms reveals a clear improvement in background rejection performance. When applied to H.E.S.S. observation data, the CNN direction reconstruction performs at a similar level as traditional methods. These results serve as a proof-of-concept for the application of CNNs to the analysis of events recorded by IACTs. (C) 2018 Published by Elsevier B.V.

Authors with CRIS profile

Idan Shilon Lehrstuhl für Physik Matthias Büchele Lehrstuhl für Physik Stefan Funk Lehrstuhl für Physik

Involved external institutions

Universität Potsdam DE Germany (DE) Humboldt-Universität zu Berlin DE 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://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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