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