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The first comprehensive Milky Way stellar mock catalogue for the Chinese Space Station Telescope Survey Camera

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摘要 The Chinese Space Station Telescope(CSST)is a cutting-edge two-meter astronomical space telescope currently under construction.Its primary Survey Camera(SC)is designed to conduct large-area imaging sky surveys using a sophisticated seven-band photometric system.The resulting data will provide unprecedented data for studying the structure and stellar populations of the Milky Way.To support the CSST development and scientific projects related to its survey data,we generate the first comprehensive Milky Way stellar mock catalogue for the CSST SC photometric system using the TRILEGAL stellar population synthesis tool.The catalogue includes approximately 12.6 billion stars,covering a wide range of stellar parameters,photometry,astrometry,and kinematics,with magnitude reaching down to g=27.5 mag in the AB magnitude system.The catalogue represents our benchmark understanding of the stellar populations in the Milky Way,enabling a direct comparison with the future CSST survey data.Particularly,it sheds light on faint stars hidden from current sky surveys.Our crowding limit analysis based on this catalogue provides compelling evidence for the extension of the CSST Optical Survey(OS)to cover low Galactic latitude regions.The strategic extension of the CSST-OS coverage,combined with this comprehensive mock catalogue,will enable transformative science with the CSST.
出处 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CAS CSCD 2023年第11期206-220,共15页 中国科学:物理学、力学、天文学(英文版)
基金 supported by the National Key R&D Program of China(Grant Nos.2021YFC2203100,and 2021YFC2203104) the science research grants from the China Manned Space Project(Grant No.CMSCSST-2021-A08) the National Natural Science Foundation of China(Grant No.12003001) the Anhui Project(Grant No.Z010118169) the support of the National Natural Science Foundation of China(Grant No.12203100) the National Natural Science Foundation of China(Grant No.12273077) the support from Padova University through the research project PRD 2021。
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