Galaxy clusters’ strong gravitational lensing: toward the automation of multiple images set detection via photometric analysis of HST images

Abstract

The detection of multiple images of background sources in strong lensing systems is a crucial step for cluster mass-modeling and the inference of cosmological constraints. This work presents an end-to-end pipeline designed as a first step toward the automated identification of multiple-image sets in crowded cluster fields, encompassing the entire analysis from multi-band source detection to spectral energy distribution (SED) similarity matching. Applied to HST images of the cluster SPT-CL J0356-5337, the pipeline effectively rejected the vast majority of the 19,925 initial candidate pairs, successfully identifying known lensed images as top mutual matches. However, the complex crowded field environment, characterized by flux contamination, deblending challenges, and noise-dominated faint sources, introduced a high-scoring tail of unlensed pairs. Rather than mere limitations, these high-scoring pairs serve as valuable diagnostics, highlighting the boundaries of global extraction parameters and the need for robust cluster-member decontamination. This interpretable and transparent approach offers a complementary framework that can serve as an insightful exploration tool and holds potential for scaling up to future wide-field lensing surveys.