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The Model Analysis is based on a comparison and fit of observed air shower images with a precomputed library of images (31); another analysis technique, based on a machine learning algorithm called boosted decision trees (BDT) (32), was used to verify the results. It was trained with simulated gamma rays and with real cosmic-ray data from background fields (33). Both algorithms yield an improvement by a factor 1.5 to 1.7 in the statistical significance of faint sources compared with the standard image analysis (26), as verified with a number of other gamma-ray sources. The Model Analysis result is found to be consistent with the one obtained with the BDT analysis. Details are given in the SOM.
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The support of the Namibian authorities and of the University of Namibia in facilitating the construction and operation of H.E.S.S. is gratefully acknowledged, as is the support by the German Ministry for Education and Research (BMBF), the Max Planck Society, the French Ministry for Research, the CNRS-IN2P3 and the Astropartiele Interdisciplinary Programme of the CNRS, the UK Science and Technology Facilities Council (STFC), the IPNP of Charles University, the Polish Ministry of Science and Higher Education, the South African Department of Science and Technology and National Research Foundation, and the University of Namibia. U. Barres de Almeida is supported by the CAPES Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Foundation, Ministry of Education of Brazil. The European Associated Laboratory for Gamma-Ray Astronomy is jointly supported by CNRS and Max-Planck-Gesellschaft. We appreciate the excellent work of the technical support staff in Berlin, Durham, Hamburg, Heidelberg, Palaiseau, Paris, Saclay, and Namibia in the construction and operation of the equipment.
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