Toward High-Precision Astrometry with CSST Using Multi-Gaussian Fitting of PSF

TL;DR

This paper demonstrates that modeling the CSST PSF with a three-Gaussian approach significantly improves astrometric accuracy, achieving sub-milliarcsecond precision and better performance than existing methods in crowded fields.

Contribution

The study introduces a multi-Gaussian PSF modeling technique for CSST that enhances astrometric precision and computational efficiency over traditional methods.

Findings

Centroiding accuracy below 1 mas in sparse fields

Proper motion errors under 1.0 mas/yr with five observations

Position measurement precision surpasses SExtractor and DOLPHOT in crowded fields

Abstract

The Chinese Space Station Survey Telescope (CSST) presents significant potential for high-precision astrometry. In this study, we show that the point spread function (PSF) modeled by the discrete PSF with Multi-Gaussian function can effectively enhance the astrometric accuracy. We determine that the PSF profile can be accurately modeled by three Gaussians, which takes advantage of reduced computational complexity in PSF convolution. In sparse star fields, the lowest centering accuracy we obtain after aberration correction can be below 1 mas. We find that the proper motion errors remain below 1.0 mas/yr for point sources with five observations and approximately 0.8 mas/yr for seven observations with a time baseline of around 3.5 years. We finally demonstrate that the precision of our position measurements for stars fainter than 21 mag in the simulated CSST crowded field is better than the results from both SExtractor and DOLPHOT.