TY  - CHAP
A1  - Tatischeff, V.
A1  - De Angelis, A.
A1  - Tavani, M.
A1  - Grenier, I.
A1  - Oberlack, U.
A1  - Hanlon, L.
A1  - Walter, R.
A1  - Argan, A.
A1  - von Ballmoos, P.
A1  - Bulgarelli, A.
A1  - Donnarumma, I.
A1  - Hernanz, Margarita
A1  - Kuvvetli, I.
A1  - Mallamaci, M.
A1  - Pearce, M.
A1  - Zdziarski, A.
A1  - Aboudan, A.
A1  - Ajello, M.
A1  - Ambrosi, G.
A1  - Bernard, D.
A1  - Bernardini, E.
A1  - Bonvicini, V.
A1  - Brogna, A.
A1  - Branchesi, M.
A1  - Budtz-Jorgensen, C.
A1  - Bykov, A.
A1  - Campana, R.
A1  - Cardillo, M.
A1  - Ciprini, S.
A1  - Coppi, P.
A1  - Cumani, P.
A1  - da Silva, R. M. Curado
A1  - De Martino, D.
A1  - Diehl, R.
A1  - Doro, M.
A1  - Fioretti, V.
A1  - Funk, S.
A1  - Ghisellini, G.
A1  - Giordano, F.
A1  - Grove, J. E.
A1  - Hamadache, C.
A1  - Hartmann, D. H.
A1  - Hayashida, M.
A1  - Isern, J.
A1  - Kanbach, G.
A1  - Kiener, J.
A1  - Knodlseder, J.
A1  - Labanti, C.
A1  - Laurent, P.
A1  - Leising, M.
A1  - Limousin, O.
A1  - Longo, F.
A1  - Mannheim, K.
A1  - Marisaldi, M.
A1  - Martinez, M.
A1  - Mazziotta, M. N.
A1  - McEnery, J. E.
A1  - Mereghetti, S.
A1  - Minervini, G.
A1  - Moiseev, A.
A1  - Morselli, A.
A1  - Nakazawa, K.
A1  - Orleanski, P.
A1  - Paredes, J. M.
A1  - Patricelli, B.
A1  - Peyre, J.
A1  - Piano, G.
A1  - Pohl, Martin
A1  - Rando, R.
A1  - Roncadelli, M.
A1  - Tavecchio, F.
A1  - Thompson, D. J.
A1  - Turolla, R.
A1  - Ulyanov, A.
A1  - Vacchi, A.
A1  - Wu, X.
A1  - Zoglauer, A.
ED  - DenHerder, JWA Nikzad
T1  - The e-ASTROGAM gamma-ray space observatory for the multimessenger astronomy of the 2030s
T2  - Space Telescopes and Instrumentation 2018: Ultraviolet to Gamma Ray
N2  - 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.
KW  - Gamma-ray astronomy
KW  - time-domain astronomy
KW  - space mission
KW  - Compton and pair creation telescope
KW  - gamma-ray polarization
KW  - high-energy astrophysical phenomena
Y1  - 2018
SN  - 978-1-5106-1952-4
U6  - https://doi.org/10.1117/12.2315151
SN  - 0277-786X
SN  - 1996-756X
VL  - 10699
PB  - SPIE - The International Society for Optical Engineering
CY  - Bellingham
ER  - 
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  -