A catalog of polychromatic bulge-disk decompositions of ~ 17.600 galaxies in CANDELS

TL;DR

This paper presents a large catalog of bulge-disk decompositions for approximately 17,600 galaxies in the CANDELS survey, utilizing deep learning to improve model selection and reduce systematics, enabling detailed analysis of galaxy structure and stellar populations up to redshift 2.

Contribution

It introduces a novel deep-learning based method for bulge-disk decomposition, providing the largest such catalog up to z=2 with improved accuracy and reduced contamination.

Findings

Structural properties are within 10-20% of uncertainties.

Derived stellar masses and colors for bulges and disks.

Catalog is publicly available for further scientific analysis.

Abstract

Understanding how bulges grow in galaxies is critical step towards unveiling the link between galaxy morphology and star-formation. To do so, it is necessary to decompose large sample of galaxies at different epochs into their main components (bulges and disks). This is particularly challenging, especially at high redshifts, where galaxies are poorly resolved. This work presents a catalog of bulge-disk decompositions of the surface brightness profiles of ~17.600 H-band selected galaxies in the CANDELS fields (F160W<23, 0<z<2) in 4 to 7 filters covering a spectral range of 430-1600nm. This is the largest available catalog of this kind up to z = 2. By using a novel approach based on deep-learning to select the best model to fit, we manage to control systematics arising from wrong model selection and obtain less contaminated samples than previous works. We show that the derived structural properties are within $\sim10-20\%$ of random uncertainties. We then fit stellar population models to the decomposed SEDs (Spectral Energy Distribution) of bulges and disks and derive stellar masses (and stellar mass bulge-to-total ratios) as well as rest-frame colors (U,V,J) for bulges and disks separately. All data products are publicly released with this paper and through the web page https://lerma.obspm.fr/huertas/form_CANDELS and will be used for scientific analysis in forthcoming works.