Machine-learning-based photometric redshifts for galaxies of the ESO Kilo-Degree Survey data release 2

Abstract

We have estimated photometric redshifts (z<SUB>phot</SUB>) for more than 1.1 million galaxies of the public European Southern Observatory (ESO) Kilo-Degree Survey (KiDS) data release 2. KiDS is an optical wide-field imaging survey carried out with the Very Large Telescope (VLT) Survey Telescope (VST) and the OmegaCAM camera, which aims to tackle open questions in cosmology and galaxy evolution, such as the origin of dark energy and the channel of galaxy mass growth. We present a catalogue of photometric redshifts obtained using the Multi-Layer Perceptron with Quasi-Newton Algorithm (MLPQNA) model, provided within the framework of the DAta Mining and Exploration Web Application REsource (DAMEWARE). These photometric redshifts are based on a spectroscopic knowledge base that was obtained by merging spectroscopic data sets from the Galaxy and Mass Assembly (GAMA) data release 2 and the Sloan Digital Sky Survey III (SDSS-III) data release 9. The overall 1σ uncertainty on ∆z = (z<SUB>spec</SUB> - z<SUB>phot</SUB>)/(1 + z<SUB>spec</SUB>) is ̃0.03, with a very small average bias of ̃0.001, a normalized median absolute deviation of ̃0.02 and a fraction of catastrophic outliers (|∆z| > 0.15) of ̃0.4 per cent.