Precision MARS Mass Reconstruction of Abell 2744: Synergizing the Largest Strong Lensing and Densest Weak Lensing Datasets from JWST

TL;DR

This paper introduces a high-resolution mass model of Abell 2744 using JWST data, combining extensive strong and weak lensing datasets, revealing detailed cluster structure and demonstrating the effectiveness of a deep learning-based free-form reconstruction method.

Contribution

The study presents the first high-resolution mass reconstruction of Abell 2744 combining the largest strong and densest weak lensing datasets from JWST, utilizing a deep learning algorithm with 300,000 parameters.

Findings

Revealed an isosceles triangular mass structure of Abell 2744.

Achieved the smallest scatter in multiple image reconstructions to date.

Demonstrated rapid convergence of a deep learning-based mass modeling method.

Abstract

We present a new high-resolution free-form mass model of Abell 2744, combining both weak-lensing (WL) and strong-lensing (SL) datasets from JWST. The SL dataset comprises 286 multiple images, presenting the most extensive SL constraint to date for a single cluster. The WL dataset, employing photo-$z$ selection, yields a source density of ~ 350 arcmin$^{-2}$, marking the densest WL constraint ever. The combined mass reconstruction enables the highest-resolution mass map of Abell 2744 within the ~ 1.8 Mpc$\times$1.8 Mpc reconstruction region to date, revealing an isosceles triangular structure with two legs of ~ 1 Mpc and a base of ~ 0.6 Mpc. Although our algorithm MAximum-entropy ReconStruction (${\tt MARS}$) is entirely blind to the cluster galaxy distribution, the resulting mass reconstruction remarkably well traces the brightest cluster galaxies with the five strongest mass peaks coinciding with the five most luminous cluster galaxies within $\lesssim 2''$. We do not detect any unusual mass peaks that are not traced by the cluster galaxies, unlike the findings in previous studies. Our mass model shows the smallest scatters of SL multiple images in both source (~0".05) and image (~0".1) planes, which are lower than the previous studies by a factor of ~ 4. Although ${\tt MARS}$ represents the mass field with an extremely large number of ~ 300,000 free parameters, it converges to a solution within a few hours thanks to our utilization of the deep learning technique. We make our mass and magnification maps publicly available.