Filtern
Volltext vorhanden
- nein (2)
Erscheinungsjahr
- 2018 (2) (entfernen)
Dokumenttyp
- Wissenschaftlicher Artikel (2) (entfernen)
Sprache
- Englisch (2) (entfernen)
Gehört zur Bibliographie
- ja (2)
Schlagworte
- Analysis technique (1)
- Convolutional neural networks (1)
- Deep learning (1)
- Gamma-ray astronomy (1)
- IACT (1)
- Recurrent neural networks (1)
- dark matter detectors (1)
- dark matter experiments (1)
- dwarfs galaxies (1)
- gamma ray detectors (1)
Institut
Dwarf spheroidal galaxies are among the most promising targets for detecting signals of Dark Matter (DM) annihilations. The H.E.S.S. experiment has observed five of these systems for a total of about 130 hours. The data are re-analyzed here, and, in the absence of any detected signals, are interpreted in terms of limits on the DM annihilation cross section. Two scenarios are considered: i) DM annihilation into mono-energetic gamma-rays and ii) DM in the form of pure WIMP multiplets that, annihilating into all electroweak bosons, produce a distinctive gamma-ray spectral shape with a high-energy peak at the DM mass and a lower-energy continuum. For case i), upper limits at 95% confidence level of about <sigma upsilon > less than or similar to 3 x 10(-25) cm(3) s(-1) are obtained in the mass range of 400 GeV to 1TeV. For case ii), the full spectral shape of the models is used and several excluded regions are identified, but the thermal masses of the candidates are not robustly ruled out.
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.