A cooperative approach among methods for photometric redshifts estimation: an application to KiDS data

TL;DR

This paper presents a hybrid approach combining machine learning and SED fitting techniques to improve photometric redshift estimation accuracy using KiDS survey data, demonstrating enhanced results over individual methods.

Contribution

It introduces a collaborative framework that integrates SED fitting with machine learning to refine photometric redshift estimates, a novel approach in this context.

Findings

Machine learning methods outperform SED fitting in well-sampled regions.

SED fitting provides valuable galaxy spectral type information.

Hybrid approach improves photo-z accuracy and reduces outliers.

Abstract

Photometric redshifts (photo-z's) are fundamental in galaxy surveys to address different topics, from gravitational lensing and dark matter distribution to galaxy evolution. The Kilo Degree Survey (KiDS), i.e. the ESO public survey on the VLT Survey Telescope (VST), provides the unprecedented opportunity to exploit a large galaxy dataset with an exceptional image quality and depth in the optical wavebands. Using a KiDS subset of about 25,000 galaxies with measured spectroscopic redshifts, we have derived photo-z's using i) three different empirical methods based on supervised machine learning, ii) the Bayesian Photometric Redshift model (or BPZ), and iii) a classical SED template fitting procedure (Le Phare). We confirm that, in the regions of the photometric parameter space properly sampled by the spectroscopic templates, machine learning methods provide better redshift estimates, with a lower scatter and a smaller fraction of outliers. SED fitting techniques, however, provide useful information on the galaxy spectral type which can be effectively used to constrain systematic errors and to better characterize potential catastrophic outliers. Such classification is then used to specialize the training of regression machine learning models, by demonstrating that a hybrid approach, involving SED fitting and machine learning in a single collaborative framework, can be effectively used to improve the accuracy of photo-z estimates.