Fast and accurate astronomical source deblending with Density-Peak Clustering

Abstract

Source deblending is a fundamental challenge for current and forthcoming astronomical surveys, where increasing source density and image depth lead to a growing number of overlapping detections. Accurate deblending is essential for reliable measurements of source morphology and photometry, as well as for cosmological analyses. We present a redesign of the Advanced Density Peak (ADP) clustering algorithm, tailored to the identification and separation of blended astronomical sources within detection regions. We develop a validation framework combining realistic image simulations, automatically generated ground-truth segmentation, and label-invariant metrics. ADP is assessed against ASTErIsM, an established density-based astronomical deblender, using pairwise simulations, synthetic multi-source images, and Euclid Q1 public data. In pairwise simulations, the methods show comparable performance across a broad range of source separations and flux ratios, with photometric differences typically below 1% and reaching 5-7% in the most challenging cases, without systematic bias. In multi-source simulations, ADP recovers approximately 8% more ground-truth sources, while the positions of sources identified by both methods agree at the sub-pixel level. On Euclid Q1 public data, the methods show strong agreement in segmentation area, ellipticity, position angle, and photometry, with the largest differences for the smallest and faintest sources. ADP also provides a substantial computational advantage: end-to-end benchmarks on 19200 x 19200 pixel Euclid images require approximately 16-200 s, corresponding to speedups of 12-156x relative to ASTErIsM, with median and mean improvements of 36x and 54x, respectively. These results show that ADP provides scientifically competitive deblending at substantially lower computational cost, making it a promising approach for large-scale astronomical imaging surveys.