Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks

Bilicki, M.M.BilickiHoekstra, H.H.HoekstraBrown, M. J. I.M. J. I.BrownAmaro, V.V.AmaroBlake, C.C.BlakeCAVUOTI, STEFANOSTEFANOCAVUOTIde Jong, J. T. A.J. T. A.de JongGeorgiou, C.C.GeorgiouHildebrandt, H.H.HildebrandtWolf, C.C.WolfAmon, A.A.AmonBRESCIA, MassimoMassimoBRESCIABrough, S.S.BroughCosta-Duarte, M. V.M. V.Costa-DuarteErben, T.T.ErbenGlazebrook, K.K.GlazebrookGRADO, ANIELLOANIELLOGRADOHeymans, C.C.HeymansJarrett, T.T.JarrettJoudaki, S.S.JoudakiKuijken, K.K.KuijkenLongo, G.G.LongoNAPOLITANO, NICOLA ROSARIONICOLA ROSARIONAPOLITANOParkinson, D.D.ParkinsonVellucci, C.C.VellucciVerdoes Kleijn, G. A.G. A.Verdoes KleijnWang, L.L.Wang2020-10-062020-10-0620180004-6361http://hdl.handle.net/20.500.12386/27595We 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 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 (IR) bands are added. While the fiducial four-band ugri setup gives a photo-z bias <δz/(1 + z)> = -2 × 10-4 and scatter σδz/(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 the ugri and IR magnitudes are joined into 12-band photometry spanning up to 12 μm, 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 × 10-5 and σδz/(1+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, optimised 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.STAMPAenPhotometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networksArticle10.1051/0004-6361/2017319422-s2.0-85051859213000442539800002http://arxiv.org/abs/1709.04205v2https://www.aanda.org/articles/aa/abs/2018/08/aa31942-17/aa31942-17.html2018A&A...616A..69BFIS/05 - ASTRONOMIA E ASTROFISICAERC sectors::Physical Sciences and Engineering::PE9 Universe sciences: astro-physics/chemistry/biology; solar systems; stellar, galactic and extragalactic astronomy, planetary systems, cosmology, space science, instrumentation