GRAVITATIONAL LENS MODELING with BASIS SETS

Abstract

We present a strong lensing modeling technique based on versatile basis sets for the lens and source planes. Our method uses high performance Monte Carlo algorithms, allows for an adaptive build up of complexity, and bridges the gap between parametric and pixel based reconstruction methods. We apply our method to a Hubble Space Telescope image of the strong lens system RX J1131-1231 and show that our method finds a reliable solution and is able to detect substructure in the lens and source planes simultaneously. Using mock data, we show that our method is sensitive to sub-clumps with masses four orders of magnitude smaller than the main lens, which corresponds to about 108M⊙ , without prior knowledge of the position and mass of the sub-clump. The modeling approach is flexible and maximizes automation to facilitate the analysis of the large number of strong lensing systems expected in upcoming wide field surveys. The resulting search for dark sub-clumps in these systems, without mass-to-light priors, offers promise for probing physics beyond the standard model in the dark matter sector.