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".
Mock Observations of Multiple Stellar Populations in Tidal Streams of Palomar 5 for the Chinese Space Station Survey Telescope
School of Physics and Astronomy, Sun Yat-sen University（中山大学物理学院）；CSST Science Center for the Guangdong-Hong Kong-Macau Greater Bay Area（中国空间站巡天望远镜粤港澳大湾区科学中心）
MCI: Multi-Channel Imager on the Chinese Space Station Survey Telescope
Shanghai Astronomical Observatory, Chinese Academy of Sciences（中国科学院上海天文台）；Shanghai Institute of Technical Physics, Chinese Academy of Sciences（中国科学院上海技术物理研究所）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间应用工程与技术中心）；Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences（中国科学院长春光学精密机械与物理研究所）；School of Astronomy and Space Science, Nanjing University（南京大学天文与空间科学学院）；Department of Astronomy, Xiamen University（厦门大学天文学系）；School of Physics and Astronomy, Sun Yat-Sen University（中山大学物理学院）；Yunnan Observatories, Chinese Academy of Sciences（中国科学院云南天文台）；Purple Mountain Observatory, Chinese Academy of Sciences（中国科学院紫金山天文台）；South-Western Institute for Astronomy Research, Yunnan University（云南大学西南天文研究所）；CSST Science Center for the Yangtze River Delta Region（CSST长三角科学中心）
CSST-PSFNet: A Point Spread Function Reconstruction Model for the CSST Based on Deep Learning
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）；Institute of Frontiers in Astronomy and Astrophysics, Beijing Normal University（北京师范大学前沿交叉科学研究院）；Zhejiang Lab（浙江实验室）；Center For Astrophysics, Guangzhou University（广州大学天体物理中心）
Formulation of testing gravitational redshift based on Laser Time link between China Space Station and a ground station
Time and Frequency Observation Center (TFOC), School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院时间频率观测中心）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；School of Surveying and Land Information Engineering, Henan Polytechnic University（河南理工大学测绘与土地信息工程学院）；School of Resource, Environmental Science and Engineering, Hubei University of Science and Technology（湖北科技学院资源环境科学与工程学院）；School of Surveying and Mapping, Information Engineering University（信息工程大学测绘学院）；GNSS Research Center, Wuhan University（武汉大学GNSS研究中心）；School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology（南京信息工程大学遥感与地理信息工程学院）
Characterization of the CSST Survey Camera CCDs: I. basic electro-optical performance
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；University of Chinese Academy of Sciences（中国科学院大学）；Peking University（北京大学）
Extracting redshifts from 2D slitless spectroscopic images using deep learning for the CSST galaxy survey
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；Science Center for China Space Station Telescope, National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台中国空间站望远镜科学中心）；University of Chinese Academy of Sciences（中国科学院大学）
A Multi-modal Fusion Network for Star-Galaxy Classification from CSST Simulated Datasets
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）；Institute for Frontiers in Astronomy and Astrophysics of Beijing Normal University（北京师范大学天文与空间科学研究院）；Zhejiang Lab（浙江实验室）
CPI-C: Cool Planet Imaging Coronagraph on Chinese Space Station Survey Telescope
Nanjing Institute of Astronomical Optics \& Technology, Chinese Academy of Sciences, Nanjing 210042, China；CAS Key Laboratory of Astronomical Optics \& Technology, Nanjing Institute of Astronomical Optics \& Technology, Nanjing 210042,China Received 20xx month day
RPCA: a Resolved PSF reconstruction method for undersampled images
Shanghai Astronomical Observatory (SHAO)（上海天文台）；Purple Mountain Observatory, Chinese Academy of Sciences（中国科学院紫金山天文台）；IRFU, CEA, Université Paris-Saclay（法国原子能和替代能源署 IRFU 实验室，巴黎萨克雷大学）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）
