Implications from simulated strong gravitational lensing systems: constraining cosmological parameters using Gaussian Processes
Tonghua Liu, Shuo Cao, Jia Zhang, Shuaibo Geng, Yuting Liu, Xuan Ji,, and Zong-Hong Zhu

TL;DR
This paper demonstrates how future strong gravitational lensing data can be used with Gaussian Processes to independently constrain cosmological parameters like matter density, achieving precision comparable to Planck results.
Contribution
It introduces a model-independent reconstruction method using Gaussian Processes on simulated LSST strong lensing data to constrain cosmological parameters without assuming parametric models.
Findings
Estimated $oldsymbol{ ext{Ω}_m}$ with $oldsymbol{ ext{ΔΩ}_m ext{≈0.015}}$ in ΛCDM.
Projected $oldsymbol{ ext{ΔΩ}_m ext{≈0.030}}$ for DGP modified gravity.
Identified and quantified key systematic errors affecting constraints.
Abstract
Strongly gravitational lensing systems (SGL) encodes cosmology information in source/lens distance ratios , which can be used to precisely constrain cosmological parameters. In this paper, based on future measurements of 390 strong lensing systems from the forthcoming LSST survey, we have successfully reconstructed the distance ratio (with the source redshift ), directly from the data without assuming any parametric form. A recently developed method based on model-independent reconstruction approach, Gaussian Processes (GP) is used in our study of these strong lensing systems. Our results show that independent measurement of the matter density parameter () could be expected from such strong lensing statistics. More specifically, one can expect to be estimated at the…
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