SourceREACH (Source REconstruction of Arcs behind Cluster Halos): A New Source Reconstruction Algorithm Optimized for Giant Arcs and Galaxy Cluster Lenses

TL;DR

This paper presents SourceREACH, an efficient and accurate source reconstruction algorithm tailored for giant arcs in galaxy cluster lensing, improving modeling of extended lensed images with high-resolution data.

Contribution

The paper introduces a novel source reconstruction method optimized for giant arcs, combining deconvolution, de-lensing, and regression techniques for better accuracy and efficiency.

Findings

K Nearest Neighbor Regression offers optimal noise smoothing and detail preservation.

The method performs well on both mock and real data, including Abell 370.

It effectively accounts for varying resolution across the source plane.

Abstract

We introduce a new algorithm designed for use with extended lensed images, specifically giant arcs lensed by galaxy clusters. These highly magnified images contain important information about both the mass distribution of the cluster and the properties of the background source, but modeling them requires significant computational effort. Our new source reconstruction methodology is designed to be accurate and efficient for high-resolution observations in which point spread function effects are not significant. The overall process deconvolves the observed image by the point spread function, de-lenses the image pixels, and uses interpolation or regression with smoothing to determine the model source. By working with de-lensed points, the method accounts for varying resolution across the source plane. We evaluate the speed and accuracy of different interpolation and regression methods using both mock data and real data for the giant arc in Abell 370. We find that utilizing K Nearest Neighbor Regression results in the best balance of noise smoothing and preservation of compact detail in the source.