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Institute
Collaboration, H. E. S. S. ; Abramowski, Attila ; Aharonian, Felix A. ; Benkhali, Faical Ait ; Akhperjanian, A. G. ; Angüner, Ekrem Oǧuzhan ; Anton, Gisela ; Balenderan, Shangkari ; Balzer, Arnim ; Barnacka, Anna ; Becherini, Yvonne ; Tjus, J. Becker ; Bernlöhr, K. ; Birsin, E. ; Bissaldi, E. ; Biteau, Jonathan ; Boettcher, Markus ; Boisson, Catherine ; Bolmont, J. ; Bordas, Pol ; Brucker, J. ; Brun, Francois ; Brun, Pierre ; Bulik, Tomasz ; Carrigan, Svenja ; Casanova, Sabrina ; Cerruti, M. ; Chadwick, Paula M. ; Chalme-Calvet, R. ; Chaves, Ryan C. G. ; Cheesebrough, A. ; Chretien, M. ; Colafrancesco, Sergio ; Cologna, Gabriele ; Conrad, Jan ; Couturier, C. ; Cui, Y. ; Dalton, M. ; Daniel, M. K. ; Davids, I. D. ; Degrange, B. ; Deil, C. ; de Wilt, P. ; Dickinson, H. J. ; Djannati-Ataï, A. ; Domainko, W. ; Dubus, G. ; Dutson, K. ; Dyks, J. ; Dyrda, M. ; Edwards, T. ; Egberts, Kathrin ; Eger, P. ; Espigat, P. ; Farnier, C. ; Fegan, S. ; Feinstein, F. ; Fernandes, M. V. ; Fernandez, D. ; Fiasson, A. ; Fontaine, G. ; Foerster, A. ; Fuessling, M. ; Gajdus, M. ; Gallant, Y. A. ; Garrigoux, T. ; Giavitto, G. ; Giebels, B. ; Glicenstein, J. F. ; Grondin, M. -H. ; Grudzinska, M. ; Haeffner, S. ; Hahn, J. ; Harris, J. ; Heinzelmann, G. ; Henri, G. ; Hermann, G. ; Hervet, O. ; Hillert, A. ; Hinton, James Anthony ; Hofmann, W. ; Hofverberg, P. ; Holler, M. ; Horns, Dieter ; Jacholkowska, A. ; Jahn, C. ; Jamrozy, M. ; Janiak, M. ; Jankowsky, F. ; Jung, I. ; Kastendieck, M. A. ; Katarzynski, K. ; Katz, U. ; Kaufmann, S. ; Khelifi, B. ; Kieffer, M. ; Klepser, S. ; Klochkov, D. ; Kluzniak, W. ; Kneiske, T. ; Kolitzus, D. ; Komin, Nu. ; Kosack, K. ; Krakau, S. ; Krayzel, F. ; Krueger, P. P. ; Laffon, H. ; Lamanna, G. ; Lefaucheur, J. ; Lemiere, A. ; Lemoine-Goumard, M. ; Lenain, J. -P. ; Lennarz, D. ; Lohse, T. ; Lopatin, A. ; Lu, C. -C. ; Marandon, V. ; Marcowith, Alexandre ; Marx, R. ; Maurin, G. ; Maxted, N. ; Mayer, M. ; McComb, T. J. L. ; Mehault, J. ; Meintjes, P. J. ; Menzler, U. ; Meyer, M. ; Moderski, R. ; Mohamed, M. ; Moulin, Emmanuel ; Murach, T. ; Naumann, C. L. ; de Naurois, M. ; Niemiec, J. ; Nolan, S. J. ; Oakes, L. ; Ohm, S. ; Wilhelmi, E. de Ona ; Opitz, B. ; Ostrowski, M. ; Oya, I. ; Panter, M. ; Parsons, R. D. ; Arribas, M. Paz ; Pekeur, N. W. ; Pelletier, G. ; Perez, J. ; Petrucci, P. -O. ; Peyaud, B. ; Pita, S. ; Poon, H. ; Puehlhofer, G. ; Punch, M. ; Quirrenbach, A. ; Raab, S. ; Raue, M. ; Reimer, A. ; Reimer, O. ; Renaud, M. ; de los Reyes, R. ; Rieger, F. ; Rob, L. ; Romoli, C. ; Rosier-Lees, S. ; Rowell, G. ; Rudak, B. ; Rulten, C. B. ; Sahakian, V. ; Sanchez, David M. ; Santangelo, Andrea ; Schlickeiser, R. ; Schuessler, F. ; Schulz, A. ; Schwanke, U. ; Schwarzburg, S. ; Schwemmer, S. ; Sol, H. ; Spengler, G. ; Spies, F. ; Stawarz, L. ; Steenkamp, R. ; Stegmann, Christian ; Stinzing, F. ; Stycz, Kornelia ; Sushch, Iurii ; Szostek, A. ; Tavernet, J. -P. ; Tavernier, T. ; Taylor, A. M. ; Terrier, R. ; Tluczykont, M. ; Trichard, C. ; Valerius, K. ; van Eldik, C. ; van Soelen, B. ; Vasileiadis, G. ; Venter, C. ; Viana, A. ; Vincent, P. ; Voelk, H. J. ; Volpe, F. ; Vorster, M. ; Vuillaume, T. ; Wagner, S. J. ; Wagner, P. ; Ward, M. ; Weidinger, M. ; Weitzel, Q. ; White, R. ; Wierzcholska, A. ; Willmann, P. ; Woernlein, A. ; Wouters, D. ; Zabalza, V. ; Zacharias, M. ; Zajczyk, A. ; Zdziarski, A. A. ; Zech, Alraune ; Zechlin, H. -S.
Context. On March 4, 2013 the Fermi-EAT and AGILE reported a flare from the direction of the Crab nebula in which the high-energy (HE; E > 100 MeV) flux was six times above its quiescent level. Simultaneous observations in other energy bands give us hints about the emission processes during the flare episode and the physics of pulsar wind nebulae in general.
Aims. We search for variability in the emission of the Crab nebula at very-high energies (VHF,; E > 100 GeV), using contemporaneous data taken with the H.E.S.S. array of Cherenkov telescopes.
Methods. Observational data taken with the H.E.S.S. instrument on five consecutive days during the flare were analysed for the flux and spectral shape of the emission from the Crab nebula. Night-wise light curves are presented with energy thresholds of 1 TeV and 5 TeV.
Results. The observations conducted with H.E.S.S. on March 6 to March 10, 2013 show no significant changes in the flux. They limit the variation in the integral flux above 1 TeV to less than 63% and the integral flux above 5 TeV to less than 78% at a 95% confidence level.
Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data
(2018)
Shilon, I. ; Kraus, M. ; Büchele, M. ; Egberts, Kathrin ; Fischer, Tobias ; Holch, Tim Lukas ; Lohse, T. ; Schwanke, U. ; Steppa, Constantin Beverly ; Funk, Stefan
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.