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Institute
Gamma-ray line signatures can be expected in the very-high-energy (E-gamma > 100 GeV) domain due to self-annihilation or decay of dark matter (DM) particles in space. Such a signal would be readily distinguishable from astrophysical gamma-ray sources that in most cases produce continuous spectra that span over several orders of magnitude in energy. Using data collected with the H. E. S. S. gamma-ray instrument, upper limits on linelike emission are obtained in the energy range between similar to 500 GeV and similar to 25 TeV for the central part of the Milky Way halo and for extragalactic observations, complementing recent limits obtained with the Fermi-LAT instrument at lower energies. No statistically significant signal could be found. For monochromatic gamma-ray line emission, flux limits of (2 x 10(-7)-2 x 10(-5)) m(-2)s(-1)sr(-1) and (1 x 10(-8)- 2 x 10(-6)) m(-2)s(-1)sr(-1) are obtained for the central part of the Milky Way halo and extragalactic observations, respectively. For a DM particle mass of 1 TeV, limits on the velocity- averaged DM annihilation cross section <sigma upsilon >(chi chi ->gamma gamma) reach similar to 10(-27)cm(3)s(-1), based on the Einasto parametrization of the Galactic DM halo density profile. DOI: 10.1103/PhysRevLett.110.041301
Very high-energy gamma-ray emission from PKS 0447-439 was detected with the H. E. S. S. Cherenkov telescope array in December 2009. This blazar is one of the brightest extragalactic objects in the Fermi bright source list and has a hard spectrum in the MeV to GeV range. In the TeV range, a photon index of 3.89 +/- 0.37 (stat) +/- 0.22 (sys) and a flux normalisation at 1 TeV, phi(1) (TeV) = (3.5 +/- 1.1(stat) +/- 0.9(sys)) x 10(-13) cm(-2) s(-1) TeV-1 were found. The detection with H. E. S. S. triggered observations in the X-ray band with the Swift and RXTE telescopes. Simultaneous UV and optical data from Swift UVOT and data from the optical telescopes ATOM and ROTSE are also available. The spectrum and light curve measured with H. E. S. S. are presented and compared to the multi-wavelength data at lower energies. A rapid flare is seen in the Swift XRT and RXTE data, together with a flux variation in the UV band, at a time scale of the order of one day. A firm upper limit of z < 0.59 on the redshift of PKS 0447-439 is derived from the combined Fermi-LAT and H. E. S. S. data, given the assumptions that there is no upturn in the intrinsic spectrum above the Fermi-LAT energy range and that absorption on the extragalactic background light (EBL) is not weaker than the lower limit provided by current models. The spectral energy distribution is well described by a simple one-zone synchrotron self-Compton scenario, if the redshift of the source is less than z less than or similar to 0.4.
Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data
(2018)
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.