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  - 
TY  - JOUR
A1  - Steppa, Constantin Beverly
A1  - Holch, Tim Lukas
T1  - HexagDLy-Processing hexagonally sampled data with CNNs in PyTorch
JF  - SoftwareX
N2  - HexagDLy is a Python-library extending the PyTorch deep learning framework with convolution and pooling operations on hexagonal grids. It aims to ease the access to convolutional neural networks for applications that rely on hexagonally sampled data as, for example, commonly found in ground-based astroparticle physics experiments.
KW  - Convolutional neural networks
KW  - Hexagonal grid
KW  - PyTorch
KW  - Astroparticle physics
Y1  - 2019
U6  - https://doi.org/10.1016/j.softx.2019.02.010
SN  - 2352-7110
VL  - 9
SP  - 193
EP  - 198
PB  - Elsevier
CY  - Amsterdam
ER  -