Improving Lens Modelling from Ground-Based Imaging with Deconvolution

Abstract

Accurate and precise mass modelling of strong lensing systems is essential for extracting insights into cosmology, galaxy environments, and the nature of dark matter. The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), which has mapped $\sim$1000 square degrees of the sky, discovered over 1000 definite or probable galaxy-scale gravitational lens candidates. As the resolution of ground-based imaging poses challenges for accurate lens modelling, high-resolution imaging is traditionally sought after for detailed analyses of these systems. In this work, we instead propose applying the STARRED algorithm, which leverages starlet wavelet regularisation, as a preprocessing step before lens modelling to maximise the scientific output of the large HSC-SSP sample. We test this approach on seven HSC-like mock lensing systems with different lensing configurations. Our results show that the resolution boost from $\sim$0.6" to $\sim$0.15" enables improved accuracy and sub-3% precision measurements of key lensing parameters such as the Einstein radius and the mass axis ratio for ground-based images. These results pave the way for accurate measurements of key strong lensing quantities from a large sample of ground-based observations.

12 pages, 6 figures, 2 appendices. Accepted for publication in MDPI Galaxies' Strong Gravitational Lens Modeling special issue