4.7 Article

MoMaF: the Mock Map Facility

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出版社

OXFORD UNIV PRESS
DOI: 10.1111/j.1365-2966.2005.09019.x

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methods : numerical; astronomical data bases : miscellaneous; galaxies : statistics; large-scale structure of Universe

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We present the Mock Map Facility, a powerful tool for converting theoretical outputs of hierarchical galaxy formation models into catalogues of virtual observations. The general principle is straightforward: mock observing cones can be generated using semi-analytically post-processed snapshots of cosmological N-body simulations. These cones can then be projected to synthesize mock sky images. To this end, the paper describes in detail an efficient technique for creating such mock cones and images from the galaxies in cosmological simulations (GALICS) semi-analytic model, providing the reader with an accurate quantification of the artefacts it introduces at every step. We show that replication effects introduce a negative bias on the clustering signal - typically peaking at less than 10 per cent around the correlation length. We also thoroughly discuss how the clustering signal is affected by finite-volume effects, and show that it vanishes at scales larger than approximately one-tenth of the simulation box size. For the purpose of analysing our method, we show that number counts and redshift distributions obtained with GALICS/MOMAF compare well with K-band observations and the two-degree field galaxy redshift survey. Given finite-volume effects, we also show that the model can reproduce the automatic plate measuring machine angular correlation function. The MOMAF results discussed here are made publicly available to the astronomical community through a public data base. Moreover, a user-friendly Web interface (http://galics.iap.fr) allows any user to recover her/his own favourite galaxy samples through simple SQL queries. The flexibility of this tool should permit a variety of uses ranging from extensive comparisons between real observations and those predicted by hierarchical models of galaxy formation, to the preparation of observing strategies for deep surveys and tests of data processing pipelines.

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