Photometric redshifts for the Kilo-Degree Survey. Machine-learning
  analysis with artificial neural networks
  We present a machine-learning photometric redshift analysis of the
Kilo-Degree Survey Data Release 3, using two neural-network based techniques:
ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets,
these ML codes provide photo-zs of quality comparable to, if not better than,
those from the BPZ code, at least up to zphot<0.9 and r<23.5. At the bright end
of r<20, where very complete spectroscopic data overlapping with KiDS are
available, the performance of the ML photo-zs clearly surpasses that of BPZ,
currently the primary photo-z method for KiDS.
  Using the Galaxy And Mass Assembly (GAMA) spectroscopic survey as
calibration, we furthermore study how photo-zs improve for bright sources when
photometric parameters additional to magnitudes are included in the photo-z
derivation, as well as when VIKING and WISE infrared bands are added. While the
fiducial four-band ugri setup gives a photo-z bias $\delta z=-2e-4$ and scatter
$\sigma_z<0.022$ at mean z = 0.23, combining magnitudes, colours, and galaxy
sizes reduces the scatter by ~7% and the bias by an order of magnitude. Once
the ugri and IR magnitudes are joined into 12-band photometry spanning up to 12
$\mu$, the scatter decreases by more than 10% over the fiducial case. Finally,
using the 12 bands together with optical colours and linear sizes gives $\delta
z<4e-5$ and $\sigma_z<0.019$.
  This paper also serves as a reference for two public photo-z catalogues
accompanying KiDS DR3, both obtained using the ANNz2 code. The first one, of
general purpose, includes all the 39 million KiDS sources with four-band ugri
measurements in DR3. The second dataset, optimized for low-redshift studies
such as galaxy-galaxy lensing, is limited to r<20, and provides photo-zs of
much better quality than in the full-depth case thanks to incorporating optical
magnitudes, colours, and sizes in the GAMA-calibrated photo-z derivation.
