@article{ShilonKrausBuecheleetal.2018,
  author    = {Shilon, I. and Kraus, M. and B{\"u}chele, M. and Egberts, Kathrin and Fischer, Tobias and Holch, Tim Lukas and Lohse, T. and Schwanke, U. and Steppa, Constantin Beverly and Funk, Stefan},
  title     = {Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data},
  series = {Astroparticle physics},
  volume    = {105},
  journal   = {Astroparticle physics},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0927-6505},
  doi       = {10.1016/j.astropartphys.2018.10.003},
  pages     = {44 -- 53},
  year      = {2018},
  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.},
  language  = {en}
}
@article{SteppaHolch2019,
  author    = {Steppa, Constantin Beverly and Holch, Tim Lukas},
  title     = {HexagDLy-Processing hexagonally sampled data with CNNs in PyTorch},
  series = {SoftwareX},
  volume    = {9},
  journal   = {SoftwareX},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2352-7110},
  doi       = {10.1016/j.softx.2019.02.010},
  pages     = {193 -- 198},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}