CSST strong lensing preparation: cosmological constraints forecast from CSST galaxy-scale strong lensing

ABSTRACT Strong gravitational lensing by galaxies is a powerful tool for studying cosmology and galaxy structure. The China Space Station Telescope (CSST) will revolutionize this field by discovering up to $\sim$100 000 galaxy-scale strong lenses, a huge increase over current samples. To harness the statistical power of this vast data set, we forecast its cosmological constraining power using the gravitational-dynamical mass combination method, which jointly exploits strong lensing observables and stellar kinematics of the lens galaxy to constrain cosmological distance ratios. We create a realistic simulated lens sample and test how uncertainties in redshift and velocity dispersion measurements affect results under ideal, optimistic, and pessimistic scenarios. We find that increasing the sample size from 100 to 10 000 systems dramatically improves precision: in the $\Lambda$CDM model, the uncertainty on the matter density parameter, $\Omega _\mathrm{ m}$, drops from 0.2 to 0.01; in the wCDM model, the uncertainty on the dark energy equation of state, w, decreases from 0.3 to 0.04. With 10 000 lenses, our constraints on dark energy are about twice as tight as those from the latest DESI BAO measurements. We compare two statistical frameworks for parameter inference: the Non-Hierarchical Bayesian Model and the Hierarchical Bayesian Model (HBM). Whilst both achieve comparable precision, the HBM provides a more physically motivated and self-consistent framework by simultaneously constraining the intrinsic scatter of the lens parameters, which makes it particularly powerful for population-level analyses. This work establishes an efficient and scalable framework for cosmological analysis with next-generation strong lensing surveys.