Enabling Non-Parametric Strong Lensing Models to Derive Reliable Cluster Mass Distributions. WSLAP+

Abstract

In the strong lensing regime non-parametric lens models struggle to achieve sufficient angular resolution for a meaningful derivation of the central cluster mass distribution. The problem lies mainly with cluster members which perturb lensed images and generate additional images, requiring high resolution modeling, even though we mainly wish to understand the relatively smooth cluster component. The required resolution is not achievable because the separation between lensed images is several times larger than the deflection angles by member galaxies, even for the deepest data. Here we bypass this limitation by incorporating a simple physical prior for member galaxies, using their observed positions and their luminosity scaled masses. This galaxy contribution is added to a relatively coarse Gaussian pixel grid for modeling the cluster mass distribution, extending our established WSLAP code (Diego et al. 2007). We test this new code with a simulation based on A1689, using the pixels belonging to multiply-lensed images and the observed member galaxies. Dealing with the cluster members this way leads to convergent solutions, without resorting to regularization, reproducing well the input cluster and substructures. We highlight the ability of this method to recover dark sub-components of the cluster, unrelated to member galaxies. Such anomalies can provide clues to the nature of invisible dark matter, but are hard to discover using parametrized models where substructures are defined by the visible data. With our increased resolution and stability we show, for the first time, that non-parametric models can be made sufficiently precise to locate multiply-lensed systems, thereby achieving fully self-consistent solutions without reliance on input systems from less objective means.