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

TL;DR

This paper forecasts cosmological constraints from CSST's galaxy-scale strong lensing (GGSL) sample using the gravitational-dynamical mass combination, and develops a scalable analysis framework to handle large data volumes. It compares two parameter-estimation strategies—Bayesian Hierarchical Modeling (BHM) and MultiNest—for a simulated 10,000-lens catalog drawn from a larger population, incorporating observational uncertainties and lens-population scatter. The results show that with 10,000 lenses, constraints on $Ω_m$ can reach about $0.01$ in $Λ$CDM and $w$ to about $0.04$ in $w$CDM, with dark-energy constraints approaching or beating DESI BAO, while data quality—especially redshift precision—significantly governs dynamic dark energy sensitivity. The study provides a robust, scalable framework for CSST strong-lensing cosmology and highlights the speed–robustness tradeoffs in large-scale Bayesian inference for future lens surveys.

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 $Λ$CDM model, the uncertainty on the matter density parameter, $Ω_m$, 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.

Figures (2)

• Figure 1: Dependence of cosmological parameter constraints on the size of the GGSL sample, inferred under the "Ideal case" scenario for observational uncertainties in redshifts and velocity dispersion using Bayesian Hierarchical Modeling. The left panel shows the evolution of constraint precision on the matter density parameter $\Omega_m$ for the $\Lambda$CDM, $w$CDM, and $w_0w_a$CDM models. The right panel shows the constraint precision on the dark energy equation of state parameter $w$ in the $w$CDM model.
• Figure 2: One- and two-dimensional posterior distributions of cosmological and lens parameters from 10,000 GGSL systems, inferred under the "Optimistic case" scenario using Bayesian Hierarchical Modeling. Contours enclose the 68% and 95% confidence levels. The grey dashed lines indicate the benchmark input values. Cosmological models are color-coded: $\Lambda$CDM (orange), $w$CDM (blue), and $w_0w_a$CDM (green).