N.A. Walton, F. Figueras, L. Balaguer-Núñez and C. Soubiran (eds)
EAS Publications Series, 67–68 (2014) 355-355
The Gaia Object Generator (GOG)
Departament d'Astronomiaa i Metereologia, Universitat de Barcelona (IEEC-UB), Martí Franquès 1, 08028 Barcelona, Spain
Gaia, a cornerstone ESA mission, will produce a three-dimensional map of our Galaxy. It was launched in December 2013, and it is expected to provide an important improvement in our understanding of the structure, composition and evolution of the Galaxy. During its five years of data collection, Gaia is expected to transmit to Earth some 150 terabytes of raw data, producing a catalogue of some 109 individual objects. After on-ground processing the full catalogue is expected to be in the range of one to two petabytes of data. Preparation for the explotation of this huge amount of data is essential, and work is being undertaken to model the expected output of Gaia in order to predict the content of the Gaia catalogue and to facilitate the production of tools required to effectively analyse the data. Therefore, the Gaia Data Processing and Analysis Consortium (DPAC) has developed a set of simulators, including a simulator called the Gaia Object Generator (GOG), which simulates the end of mission and the epoch transit catalogues, including observational errors. In 2014, CU2 has delivered a GOG end of mission catalogue, containing the potential single star observable population (with G < 20 mag) using the Gaia Universe Model, showing the effect of the observational errors. The GOG catalogue is actively used inside CU9, which is responsible for providing access to the Gaia data to the scientific community through the Gaia archive. GOG has a direct impact in the Gaia real catalogue production, as the simulated Gaia data will be used to detect false-positives and a biased data set to validate that problems can been found. Moreover, GOG simulations will be used also to test the software which will maintain and preserve all the versions of the catalogue, and to become familiar with working with such a large and rich dataset.
© EAS, EDP Sciences, 2015