CSST Strong Lensing Preparation: Cosmological Constraints Forecast from CSST Galaxy-Scale Strong Lensing

TL;DR

This paper forecasts the cosmological constraints achievable with the upcoming CSST galaxy-scale strong lensing dataset, demonstrating significant improvements in parameter precision and comparing estimation techniques for robust analysis.

Contribution

It introduces a realistic simulation framework for CSST strong lensing data and compares two parameter estimation methods, enhancing future cosmological analyses.

Findings

Sample size increase from 100 to 10,000 improves parameter precision significantly.

Constraints on dark energy parameters are twice as tight as current DESI BAO results.

Bayesian Hierarchical Modeling offers more robust lens parameter estimates, while MultiNest is faster.

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 dataset, we forecast its cosmological constraining power using the gravitational-dynamical mass combination method. 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, $\Omega_m$, drops from 0.2 to 0.01; in the $w$CDM 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 twice as tight as those from the latest DESI BAO measurements. We also compare two parameter estimation techniques -- MultiNest sampling and Bayesian Hierarchical Modeling (BHM). While both achieve similar precision, BHM provides more robust estimates of intrinsic lens parameters, whereas MultiNest is about twice as fast. This work establishes an efficient and scalable framework for cosmological analysis with next-generation strong lensing surveys.