AStroLens: Automatic Strong-Lens Modeling of X-ray Selected Galaxy Clusters

TL;DR

AStroLens is an automated gravitational lens modeling tool that uses galaxy light distribution to map strong-lensing regions in galaxy clusters, validated against detailed models, and applied to 96 X-ray selected clusters to identify powerful lenses.

Contribution

This paper introduces AStroLens, a novel automatic lens-modeling code based solely on photometric data, enabling efficient analysis of large galaxy cluster samples.

Findings

Good agreement between AStroLens and detailed lens models.

Identification of 31 clusters with Einstein radii over 20".

Discovery of eight clusters with Einstein radii exceeding 30".

Abstract

We use AStroLens, a newly developed gravitational lens-modeling code that relies only on geometric and photometric information of cluster galaxies as input, to map the strong-lensing regions and estimate the lensing strength of 96 galaxy clusters at $z=0.5$-$0.9$. All clusters were identified during the extended Massive Cluster Survey (eMACS) based on their X-ray flux and optical appearance. Building on the well tested assumption that the distribution of both luminous and dark matter in galaxy clusters is approximately traced by the distribution of light, i.e., that light traces mass, AStroLens uses three global parameters to automatically model the deflection from strong-gravitational lensing for all galaxy clusters in this diverse sample. We test the robustness of our code by comparing AStroLens estimates derived solely from shallow optical images in two passbands with the results of in-depth lens-modeling efforts for two well studied eMACS clusters and find good agreement, both with respect to the size and the shape of the strong-lensing regime delineated by the respective critical lines. Our study finds 31 eMACS clusters with effective Einstein radii ($\theta_{E}$) in excess of 20" and eight with $\theta_{E} >$ 30", thereby underlining the value of X-ray selection for the discovery of powerful cluster lenses that complement giants like MACSJ0717 at ever-increasing redshift. As a first installment toward the public release of the eMACS sample, we list physical properties of the ten calibration clusters as well as of the ten most powerful eMACS cluster lenses, according to AStroLens.