TY  - JOUR
A1  - Shilon, I.
A1  - Kraus, M.
A1  - Büchele, M.
A1  - Egberts, Kathrin
A1  - Fischer, Tobias
A1  - Holch, Tim Lukas
A1  - Lohse, T.
A1  - Schwanke, U.
A1  - Steppa, Constantin Beverly
A1  - Funk, Stefan
T1  - Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data
JF  - Astroparticle physics
N2  - 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.
KW  - Gamma-ray astronomy
KW  - IACT
KW  - Analysis technique
KW  - Deep learning
KW  - Convolutional neural networks
KW  - Recurrent neural networks
Y1  - 2018
U6  - https://doi.org/10.1016/j.astropartphys.2018.10.003
SN  - 0927-6505
SN  - 1873-2852
VL  - 105
SP  - 44
EP  - 53
PB  - Elsevier
CY  - Amsterdam
ER  -