Volume 589, May 2016
|Number of page(s)||10|
|Section||Numerical methods and codes|
|Published online||04 April 2016|
Multi-band morpho-Spectral Component Analysis Deblending Tool (MuSCADeT): Deblending colourful objects⋆
1 Laboratoire d’astrophysique, École Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland
2 Laboratoire AIM, CEA/DSM-CNRS-Université Paris Diderot, Irfu, Service d’Astrophysique, CEA Saclay, Orme des Merisiers, 91191 Gif-sur-Yvette, France
Received: 8 December 2015
Accepted: 15 February 2016
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.
Key words: methods: data analysis / gravitational lensing: strong / surveys
Codes can be found at http://lastro.epfl.ch/page-126973.html
© ESO, 2016
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