Machine Learning based photometric redshifts for the KiDS ESO DR2
  galaxies
  We estimated photometric redshifts (zphot) for more than 1.1 million galaxies
of the ESO Public Kilo-Degree Survey (KiDS) Data Release 2. KiDS is an optical
wide-field imaging survey carried out with the VLT Survey Telescope (VST) and
the OmegaCAM camera, which aims at tackling 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 which was obtained by merging spectroscopic datasets from GAMA
(Galaxy And Mass Assembly) data release 2 and SDSS-III data release 9. The
overall 1 sigma uncertainty on Delta z = (zspec - zphot) / (1+ zspec) is ~
0.03, with a very small average bias of ~ 0.001, a NMAD of ~ 0.02 and a
fraction of catastrophic outliers (| Delta z | > 0.15) of ~0.4%.
