TY  - JOUR
A1  - Archambault, S.
A1  - Archer, A.
A1  - Benbow, W.
A1  - Bird, Ralph
A1  - Bourbeau, E.
A1  - Bouvier, A.
A1  - Buchovecky, M.
A1  - Bugaev, V.
A1  - Cardenzana, J. V.
A1  - Cerruti, M.
A1  - Ciupik, L.
A1  - Connolly, M. P.
A1  - Cui, W.
A1  - Daniel, M. K.
A1  - Errando, M.
A1  - Falcone, A.
A1  - Feng, Q.
A1  - Finley, J. P.
A1  - Fleischhack, H.
A1  - Fortson, L.
A1  - Furniss, A.
A1  - Gillanders, G. H.
A1  - Griffin, S.
A1  - Hanna, D.
A1  - Hervet, O.
A1  - Holder, J.
A1  - Hughes, G.
A1  - Humensky, T. B.
A1  - Hutten, M.
A1  - Johnson, C. A.
A1  - Kaaret, P.
A1  - Kar, P.
A1  - Kertzman, M.
A1  - Kieda, D.
A1  - Krause, M.
A1  - Lang, M. J.
A1  - Lin, T. T. Y.
A1  - Maier, G.
A1  - McArthur, S.
A1  - Moriarty, P.
A1  - Mukherjee, R.
A1  - Nieto, D.
A1  - Ong, R. A.
A1  - Otte, A. N.
A1  - Park, N.
A1  - Pohl, Martin
A1  - Popkow, A.
A1  - Pueschel, Elisa
A1  - Quinn, J.
A1  - Ragan, K.
A1  - Reynolds, P. T.
A1  - Richards, G. T.
A1  - Roache, E.
A1  - Rulten, C.
A1  - Sadeh, I.
A1  - Sembroski, G. H.
A1  - Shahinyan, K.
A1  - Staszak, D.
A1  - Telezhinsky, Igor O.
A1  - Trepanier, S.
A1  - Wakely, S. P.
A1  - Weinstein, A.
A1  - Wilcox, P.
A1  - Williams, D. A.
A1  - Zitzer, B.
T1  - Gamma-ray observations under bright moonlight with VERITAS
JF  - Astroparticle physics
N2  - Imaging atmospheric Cherenkov telescopes (IACTs) are equipped with sensitive photomultiplier tube (PMT) cameras. Exposure to high levels of background illumination degrades the efficiency of and potentially destroys these photo-detectors over time, so IACTs cannot be operated in the same configuration in the presence of bright moonlight as under dark skies. Since September 2012, observations have been carried out with the VERITAS IACTs under bright moonlight (defined as about three times the night-sky-background (NSB) of a dark extragalactic field, typically occurring when Moon illumination > 35%) in two observing modes, firstly by reducing the voltage applied to the PMTs and, secondly, with the addition of ultra-violet (UV) bandpass filters to the cameras. This has allowed observations at up to about 30 times previous NSB levels (around 80% Moon illumination), resulting in 30% more observing time between the two modes over the course of a year. These additional observations have already allowed for the detection of a flare from the 1ES 1727 + 502 and for an observing program targeting a measurement of the cosmic-ray positron fraction. We provide details of these new observing modes and their performance relative to the standard VERITAS observations. (C) 2017 Elsevier B.V. All rights reserved.
KW  - Instrumentation
KW  - Moonlight
KW  - Observing methods
KW  - VERITAS
KW  - IACT
Y1  - 2017
U6  - https://doi.org/10.1016/j.astropartphys.2017.03.001
SN  - 0927-6505
SN  - 1873-2852
VL  - 91
SP  - 34
EP  - 43
PB  - Elsevier
CY  - Amsterdam
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  -