Multiple populations detection with the Chinese Space Station Survey Telescope main survey camera
Abstract
Multiple stellar populations (MPs), characterized by star-to-star light-element abundance variations, are ubiquitous in globular clusters (GCs). Spectroscopy directly reveals these anomalies, while photometric studies, especially with the \textit{Hubble Space Telescope} (\textit{HST}), have been essential for tracing MP sequences in colour-magnitude diagrams (CMDs). However, the limited field of view of \textit{HST} confines most studies to cluster centres. The upcoming \textit{Chinese Space Station Survey Telescope} (CSST), with its wide field of view and UV-optical coverage, will enable systematic MP studies over entire clusters. We assess the capability of the CSST wide-field camera to detect and characterize MPs in GCs using realistic simulations. Synthetic stellar population models with different helium abundances () and CNO variations were used to simulate CSST observations of GCs at distances of 9.6 and 20~kpc under different exposure times. MP detectability was evaluated using CMDs in seven CSST bands and UV-optical pseudo-colour diagrams. For a GC at 9.6~kpc, the colour is highly sensitive to and CNO variations, with separations of mag for red giants and up to 0.44 mag for dwarfs. MPs can be resolved when the total UV exposure exceeds ~s and the optical exposure exceeds ~s. At 20~kpc, encompassing of Galactic GCs, CSST still retains strong diagnostic power, resolving populations with and , and separating MPs down to mag in clusters with large chemical spreads. The -- combination provides diagnostic performance comparable to the \textit{HST} F275W--F336W--F438W system. CSST will enable homogeneous MP surveys across the full spatial extent of star clusters in the Milky Way and nearby galaxies.
Comments: 22 pages, 13 figures
Cite
@article{arxiv.2605.29504,
title = {Multiple populations detection with the Chinese Space Station Survey Telescope main survey camera},
author = {Zhuohang Li and Xia Li and Hao Tian and Xin Zhang and Antonino P. Milone and Long Wang and Baitian Tang and Edoardo P. Lagioia and Chengyuan Li},
journal= {arXiv preprint arXiv:2605.29504},
year = {2026}
}