The Cherenkov Telescope Array production system for data-processing and Monte Carlo simulation
Laboratoire Univers et Particules, Université de Montpellier Place Eugène Bataillon - CC 72,
2 Max-Planck-Institut für Kernphysik, P.O. Box 103980, D-69029 Heidelberg, Germany
3 Institut de Fisica d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus, UAB, 08193 Bellaterra Barcelona, Spain
4 Deutsches Elektronen-Synchrotron, Platanenallee 6, 15738 Zeuthen, Germany
5 Laboratoire d’Annecy-le-Vieux de Physique des Particules, Université Grenoble Alpes, Universitéavoie Mont Blanc, SCNRS/IN2P3, F-74000 Annecy, France
6 CERN PH Department, CH-1211 Geneva 23 Switzerland
7 Centre de Physique des Particules de Marseille, 163 Av de Luminy Case 902, CNRS/IN2P3, 13288 Marseille, France
* e-mail: email@example.com
Published online: 17 September 2019
The Cherenkov Telescope Array (CTA) is the next-generation instrument in the field of very high energy gamma-ray astronomy. It will be composed of two arrays of Imaging Atmospheric Cherenkov Telescopes, located at La Palma (Spain) and Paranal (Chile). The construction of CTA has just started with the installation of the first telescope on site at La Palma and the first data expected by the end of 2018. The scientific operations should begin in 2022 for a duration of about 30 years. The overall amount of data produced during these operations is around 27 PB per year. The associated computing power for data processing and Monte Carlo (MC) simulations is of the order of hundreds of millions of CPU HS06 hours per year. In order to cope with these high computing requirements, we have developed a production system prototype based on the DIRAC framework, that we have intensively exploited during the past 6 years to handle massive MC simulations on the grid for the CTA design and prototyping phases. CTA workflows are composed of several inter-dependent steps, which we used to handle separately within our production system. In order to fully automatize the whole workflows execution, we have partially revised the production system by further enhancing the data-driven behavior and by extending the use of meta-data to link together the different steps of a workflow. In this contribution we present the application of the production system to the last years MC campaigns as well as the recent production system evolution, intended to obtain a fully data-driven and automatized workflow execution for efficient processing of real telescope data.
© The Authors, published by EDP Sciences, 2019
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