Crowded Cluster Cores: Algorithms for Deblending in Dark Energy Survey Images

TL;DR

The paper introduces GAIN, a new deblending software that improves object detection and photometry in crowded galaxy cluster cores, tested on Dark Energy Survey images.

Contribution

We present GAIN, a novel deblender using gradient and interpolation techniques to enhance object separation in crowded cluster images.

Findings

Improves detection completeness from 91% to 97% for faint sources.

Helps extract unbiased photometry in crowded fields.

Introduces only modest spurious detections.

Abstract

Deep optical images are often crowded with overlapping objects. This is especially true in the cores of galaxy clusters, where images of dozens of galaxies may lie atop one another. Accurate measurements of cluster properties require deblending algorithms designed to automatically extract a list of individual objects and decide what fraction of the light in each pixel comes from each object. We present new software called the Gradient And INterpolation based deblender (GAIN) as a secondary deblender to improve deblending the images of cluster cores. This software relies on using image intensity gradient and using an image interpolation technique usually used to correct flawed terrestrial digital images. We test this software on Dark Energy Survey coadd images. GAIN helps extracting unbiased photometry measurement for blended sources. It also helps improving detection completeness while introducing only a modest amount of spurious detections. For example, when applied to deep images simulated with high level of deblending difficulties, this software improves detection completeness from 91% to 97% for sources above the 10? limiting magnitude at 25.3 mag. We expect this software to be a useful tool for cluster population measurements.