CSST Dense Star Field Preparation:A Framework for Astrometry and Photometry for Dense Star Field Images Obtained by the China Space Station Telescope(CSST)  

作　　者：Yining Wang Rui Sun Tianyuan Deng Chenghui Zhao Peixuan Zhao Jiayi Yang Peng Jia Huigen Liu Jilin Zhou 

机构地区：[1]College of Electronic Information and Optical Engineering,Taiyuan University of Technology,Taiyuan 030024,China [2]School of Astronomy&Space Science,Nanjing University,Nanjing 210093,China [3]Peng Cheng Lab,Shenzhen 518066,China

出　　处：《Research in Astronomy and Astrophysics》2024年第7期158-169,共12页天文和天体物理学研究（英文版）

基　　金：financial support provided by the National Natural Science Foundation of China(NSFC,grant Nos.12173027,11973028,11933001,1803012,12150009,and 12173062);the National Key R&D Program of China(2019YFA0706601);the Major Key Project of PCL;the science research grants received from the China Manned Space Project with Nos.CMS-CSST-2021-B12,CMS-CSST-2021-B09 and CMS-CSST-2021-A01;the Square Kilometre Array(SKA)Project with No.2020SKA0110102;the Civil Aerospace Technology Research Project(D050105)。

摘　　要：The China Space Station Telescope(CSST)is a telescope with 2 m diameter,obtaining images with high quality through wide-field observations.In its first observation cycle,to capture time-domain observation data,the CSST is proposed to observe the Galactic halo across different epochs.These data have significant potential for the study of properties of stars and exoplanets.However,the density of stars in the Galactic center is high,and it is a well-known challenge to perform astrometry and photometry in such a dense star field.This paper presents a deep learning-based framework designed to process dense star field images obtained by the CSST,which includes photometry,astrometry,and classifications of targets according to their light curve periods.With simulated CSST observation data,we demonstrate that this deep learning framework achieves photometry accuracy of 2%and astrometry accuracy of 0.03 pixel for stars with moderate brightness mag=24(i band),surpassing results obtained by traditional methods.Additionally,the deep learning based light curve classification algorithm could pick up celestial targets whose magnitude variations are 1.7 times larger than magnitude variations brought by Poisson photon noise.We anticipate that our framework could be effectively used to process dense star field images obtained by the CSST.

关 键 词：techniques photometric-methods data analysis-astrometry 

分 类 号：P111[天文地球—天文学]