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

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.

References in corpus (19)

• Detailed Structural Decomposition of Galaxy Images
• From filamentary clouds to prestellar cores to the stellar IMF: Initial highlights from the Herschel Gould Belt survey
• The Sloan Lens ACS Survey. IV: the mass density profile of early-type galaxies out to 100 effective radii
• Multiple Images of a Highly Magnified Supernova Formed by an Early-Type Cluster Galaxy Lens
• The behaviour of dark matter associated with 4 bright cluster galaxies in the 10kpc core of Abell 3827
• The Effective Number Density of Galaxies for Weak Lensing Measurements in the LSST Project
• T-PHOT: A new code for PSF-matched, prior-based, multiwavelength extragalactic deconfusion photometry
• CANDELS/GOODS-S, CDFS, ECDFS: Photometric Redshifts For Normal and for X-Ray-Detected Galaxies
• MegaMorph -- multi-wavelength measurement of galaxy structure: Sérsic profile fits to galaxies near and far
• Spiral structure in nearby galaxies I. Sample, data analysis, and overview of results
• The effects of dust on the derived photometric parameters of disks and bulges in spiral galaxies
• MegaMorph -- multi-wavelength measurement of galaxy structure: physically meaningful bulge-disc decomposition of galaxies near and far
• A PCA-based automated finder for galaxy-scale strong lenses
• MegaMorph: classifying galaxy morphology using multi-wavelength Sérsic profile fits
• Improving the identification of high-z Herschel sources with position priors and optical/NIR and FIR/mm photometric redshifts
• Anti-truncated stellar light profiles in the outer regions of STAGES spiral galaxies: bulge or disc related?
• Optical-faint, Far-infrared-bright Herschel Sources in the CANDELS Fields: Ultra-Luminous Infrared Galaxies at z>1 and the Effect of Source Blending
• Accuracy of photometric redshifts for future weak lensing surveys from space
• SNIa detection in the SNLS photometric analysis using Morphological Component Analysis

Cited by in corpus (20)

• Weak lensing for precision cosmology
• Baryon Content in a Sample of 91 Galaxy Clusters Selected by the South Pole Telescope at 0.2 < z < 1.25
• SCARLET: Source separation in multi-band images by Constrained Matrix Factorization
• Recovery of 21 cm intensity maps with sparse component separation
• Photometry of high-redshift blended galaxies using deep learning
• Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks
• Deblending galaxy superpositions with branched generative adversarial networks
• Deblending galaxies with Variational Autoencoders: a joint multi-band, multi-instrument approach
• Strong lensing in UNIONS: Toward a pipeline from discovery to modeling
• Dark matter dynamics in Abell 3827: new data consistent with standard Cold Dark Matter
• Imaging Extended Emission-Line Regions of Obscured AGN with the Subaru Hyper Suprime-Cam Survey
• SLITronomy: towards a fully wavelet-based strong lensing inversion technique
• Sparse Lens Inversion Technique (SLIT): lens and source separability from linear inversion of the source reconstruction problem
• Image deconvolution and PSF reconstruction with STARRED: a wavelet-based two-channel method optimized for light-curve extraction
• Bayesian photometric redshifts of blended sources
• A test for skewed distributions of dark matter and a possible detection in galaxy cluster Abell 3827
• Deep Transfer Learning for Blended Source Identification in Galaxy Survey Data
• Score-matching neural networks for improved multi-band source separation
• Gaussian Mixture Models for Blended Photometric Redshifts
• Decomposition of stellar populations in CosmoDC2 galaxies using SCARLET and Deep Learning