摘要：We present a machine-learning photometric redshift (ML photo-z) analysis of the Kilo-Degree Survey Data Release 3 (KiDS DR3), 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 Bayesian Photometric Redshift (BPZ) code, at least up tozphot≲ 0.9 andr≲ 23.5. At the bright end ofr≲ 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-zmethod 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-zderivation, as well as when VIKING and WISE infrared (IR) bands are added. While the fiducial four-bandugrisetup gives a photo-zbias 〈δz/(1 +z)〉 = −2 × 10−4and scatterσδz/(1+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 theugriand IR magnitudes are joined into 12-band photometry spanning up to 12μm, the scatter decreases by more than 10% over the fiducial case. Finally, using the 12 bands together with optical colours and linear sizes gives 〈δz/(1 +z)〉 < 4 × 10−5andσδz/(1+z)< 0.019. This paper also serves as a reference for two public photo-zcatalogues accompanying KiDS DR3, both obtained using the ANNz2 code. The first one, of general purpose, includes all the 39 million KiDS sources with four-bandugrimeasurements in DR3. The second dataset, optimised for low-redshift studies such as galaxy-galaxy lensing, is limited tor≲ 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-zderivation.
关键词：engalaxies: distances and redshiftscatalogslarge-scale structure of Universemethods: data analysismethods: numericalmethods: statistical
