Modelling strong lenses from wide-field ground-based observations in KiDS and GAMA. Issue 1 (14th January 2023)

• Record Type: Journal Article
• Authors: Knabel, Shawn; Holwerda, B W; Nightingale, J; Treu, T; Bilicki, M; Brough, S; Driver, S; Finnerty, L; Haberzettl, L; Hegde, S; Hopkins, A M; Kuijken, K; Liske, J; Pimblett, A K; Steele, R C; Wright, A H
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 520:Issue 1(2023)
• Page Start: 804
• Page End: 827
• Publication Date: 2023-01-14
• Subjects: gravitational lensing: strong -- methods: observational -- galaxies: elliptical and lenticular, cD -- galaxies: evolution -- galaxies: structure; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stad133
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: Despite the success of galaxy-scale strong gravitational lens studies with Hubble-quality imaging, a 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 modelling 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 modelling software pyautolens to perform our analysis. We demonstrate the feasibility of strong lens modelling 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-fitting 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.