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arXiv · 1901.03485

The analysis of effective galaxies number count for Chinese Space Station Optical Survey(CSS-OS) by image simulation

Xin Zhang·Li Cao·Xianmin Meng
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Abstract

The Chinese Space Station Optical Survey (CSS-OS) is a mission to explore the vast universe. This mission will equip a 2-meter space telescope to perform a multi-band NUV-optical large area survey (over 40% of the sky) and deep survey (~1% of the sky) for the cosmological and astronomical goals. Galaxy detection is one of the most important methods to achieve scientific goals. In this paper, we evaluate the galaxy number density for CSS-OS in i band (depth, i ~26 for large area survey and ~27 for the deep survey, point source, 5-sigma by the method of image simulation. We also compare galaxies detected by CSS-OS with that of LSST (i~27, point source, 5-sigma. In our simulation, the HUDF galaxy catalogs are used to create mock images due to long enough integration time which meets the completeness requirements of the galaxy analysis for CSS-OS and LSST. The galaxy surface profile and spectrum are produced by the morphological information, photometric redshift and SEDs from the catalogs. The instrumental features and the environmental condition are also considered to produce the mock galaxy images. The galaxies of CSS-OS and LSST are both extracted by SExtractor from the mock i band image and matched with the original catalog. Through the analysis of the extracted galaxies, we find that the effective galaxy number count is ~13 arcmin^-2, ~40 arcmin^-2 and ~42 arcmin^-2 for CSS-OS large area survey, CSS-OS deep survey and LSST, respectively. Moreover, CSS-OS shows the advantage in small galaxy detection with high spatial resolution, especially for the deep survey: about 20% of the galaxies detected by CSS-OS deep survey are not detected by LSST, and they have a small effective radius of re < 0.3".

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Xin Zhang, Li Cao, Xianmin Meng. 2019-01-11. The analysis of effective galaxies number count for Chinese Space Station Optical Survey(CSS-OS) by image simulation. https://doi.org/10.1007/s10509-019-3501-8

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