Multi-band morpho-Spectral Component Analysis Deblending Tool (MuSCADeT): Deblending colourful objects

TL;DR

MuSCADeT is a novel automated algorithm that uses morpho-spectral analysis to effectively deblend and separate multi-band astronomical images, outperforming traditional methods especially in complex scenarios like gravitational lensing.

Contribution

The paper introduces MuSCADeT, a new model-independent, automated deblending algorithm leveraging morpho-spectral sparsity, applicable to various astronomical imaging challenges.

Findings

Successfully separates highly blended objects in simulations.

Robust against spectral energy distribution variations.

Outperforms traditional profile-fitting techniques in real data.

Abstract

We introduce a new algorithm for colour separation and deblending of multi-band astronomical images called MuSCADeT which is based on Morpho-spectral Component Analysis of multi-band images. The MuSCADeT algorithm takes advantage of the sparsity of astronomical objects in morphological dictionaries such as wavelets and their differences in spectral energy distribution (SED) across multi-band observations. This allows us to devise a model independent and automated approach to separate objects with different colours. We show with simulations that we are able to separate highly blended objects and that our algorithm is robust against SED variations of objects across the field of view. To confront our algorithm with real data, we use HST images of the strong lensing galaxy cluster MACS J1149+2223 and we show that MuSCADeT performs better than traditional profile-fitting techniques in deblending the foreground lensing galaxies from background lensed galaxies. Although the main driver for our work is the deblending of strong gravitational lenses, our method is fit to be used for any purpose related to deblending of objects in astronomical images. An example of such an application is the separation of the red and blue stellar populations of a spiral galaxy in the galaxy cluster Abell 2744. We provide a python package along with all simulations and routines used in this paper to contribute to reproducible research efforts. Codes can be found at http://lastro.epfl.ch/page-126973.html.