@inproceedings{TatischeffDeAngelisTavanietal.2018,
  author    = {Tatischeff, V. and De Angelis, A. and Tavani, M. and Grenier, I. and Oberlack, U. and Hanlon, L. and Walter, R. and Argan, A. and von Ballmoos, P. and Bulgarelli, A. and Donnarumma, I. and Hernanz, Margarita and Kuvvetli, I. and Mallamaci, M. and Pearce, M. and Zdziarski, A. and Aboudan, A. and Ajello, M. and Ambrosi, G. and Bernard, D. and Bernardini, E. and Bonvicini, V. and Brogna, A. and Branchesi, M. and Budtz-Jorgensen, C. and Bykov, A. and Campana, R. and Cardillo, M. and Ciprini, S. and Coppi, P. and Cumani, P. and da Silva, R. M. Curado and De Martino, D. and Diehl, R. and Doro, M. and Fioretti, V. and Funk, S. and Ghisellini, G. and Giordano, F. and Grove, J. E. and Hamadache, C. and Hartmann, D. H. and Hayashida, M. and Isern, J. and Kanbach, G. and Kiener, J. and Knodlseder, J. and Labanti, C. and Laurent, P. and Leising, M. and Limousin, O. and Longo, F. and Mannheim, K. and Marisaldi, M. and Martinez, M. and Mazziotta, M. N. and McEnery, J. E. and Mereghetti, S. and Minervini, G. and Moiseev, A. and Morselli, A. and Nakazawa, K. and Orleanski, P. and Paredes, J. M. and Patricelli, B. and Peyre, J. and Piano, G. and Pohl, Martin and Rando, R. and Roncadelli, M. and Tavecchio, F. and Thompson, D. J. and Turolla, R. and Ulyanov, A. and Vacchi, A. and Wu, X. and Zoglauer, A.},
  title     = {The e-ASTROGAM gamma-ray space observatory for the multimessenger astronomy of the 2030s},
  series = {Space Telescopes and Instrumentation 2018: Ultraviolet to Gamma Ray},
  volume    = {10699},
  booktitle = {Space Telescopes and Instrumentation 2018: Ultraviolet to Gamma Ray},
  editor    = {DenHerder, JWA Nikzad},
  publisher = {SPIE - The International Society for Optical Engineering},
  address   = {Bellingham},
  isbn      = {978-1-5106-1952-4},
  issn      = {0277-786X},
  doi       = {10.1117/12.2315151},
  pages     = {15},
  year      = {2018},
  abstract  = {e-ASTROGAM is a concept for a breakthrough observatory space mission carrying a gamma-ray telescope dedicated to the study of the non-thermal Universe in the photon energy range from 0.15 MeV to 3 GeV. The lower energy limit can be pushed down to energies as low as 30 keV for gamma-ray burst detection with the calorimeter. The mission is based on an advanced space-proven detector technology, with unprecedented sensitivity, angular and energy resolution, combined with remarkable polarimetric capability. Thanks to its performance in the MeV-GeV domain, substantially improving its predecessors, e-ASTROGAM will open a new window on the non-thermal Universe, making pioneering observations of the most powerful Galactic and extragalactic sources, elucidating the nature of their relativistic outflows and their effects on the surroundings. With a line sensitivity in the MeV energy range one to two orders of magnitude better than previous and current generation instruments, e-ASTROGAM will determine the origin of key isotopes fundamental for the understanding of supernova explosion and the chemical evolution of our Galaxy. The mission will be a major player of the multiwavelength, multimessenger time-domain astronomy of the 2030s, and provide unique data of significant interest to a broad astronomical community, complementary to powerful observatories such as LISA, LIGO, Virgo, KAGRA, the Einstein Telescope and the Cosmic Explorer, IceCube-Gen2 and KM3NeT, SKA, ALMA, JWST, E-ELT, LSST, Athena, and the Cherenkov Telescope Array.},
  language  = {en}
}
@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}
}