Gravitational lens modeling with basis sets

TL;DR

This paper introduces a versatile basis set method for strong gravitational lens modeling that adapts complexity, detects substructures, and is suitable for large survey data, advancing dark matter research.

Contribution

It presents a novel, flexible lens modeling technique combining basis sets and Monte Carlo algorithms, bridging parametric and pixel-based methods, with high sensitivity to small substructures.

Findings

Successfully applied to HST data of RXJ1131-1231

Detects substructures down to 10^8 solar masses

Sensitive to dark sub-clumps without prior assumptions

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 HST image of the strong lens system RXJ1131-1231 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 $10^8 M_{\odot}$, without prior knowledge on the position and mass of the sub-clump. The modelling approach is flexible and maximises 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.