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

Abstract

Abstract 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.