Modeling Strong Lenses from Wide-Field Ground-Based Observations in KiDS and GAMA

TL;DR

This paper demonstrates automated Bayesian modeling of strong gravitational lenses using wide-field ground-based data from KiDS and GAMA, highlighting feasibility, challenges, and potential for statistical studies.

Contribution

It introduces a method for modeling strong lenses with large survey data at lower resolution, expanding the scope of lens studies beyond high-resolution imaging.

Findings

Feasibility of lens modeling with survey data demonstrated

Challenges in redshift determination and source-lens disentanglement discussed

Recommendations provided for improving future large-scale lens analyses

Abstract

Despite the success of galaxy-scale strong gravitational lens studies with Hubble-quality imaging, the number of well-studied strong lenses remains small. As a result, robust comparisons of the lens models to theoretical predictions are difficult. This motivates our application of automated Bayesian lens modeling methods to observations from public data releases of overlapping large ground-based imaging and spectroscopic surveys: Kilo-Degree Survey (KiDS) and Galaxy and Mass Assembly (GAMA), respectively. We use the open-source lens modeling software PyAutoLens to perform our analysis. We demonstrate the feasibility of strong lens modeling with large-survey data at lower resolution as a complementary avenue to studies that utilize more time-consuming and expensive observations of individual lenses at higher resolution. We discuss advantages and challenges, with special consideration given to determining background source redshifts from single-aperture spectra and to disentangling foreground lens and background source light. High uncertainties in the best-fit parameters for the models due to the limits of optical resolution in ground-based observatories and the small sample size can be improved with future study. We give broadly applicable recommendations for future efforts, and with proper application this approach could yield measurements in the quantities needed for robust statistical inference.