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Photometric redshifts (photo-z's) are fundamental in galaxy surveys toaddress different topics, from gravitational lensing and dark matterdistribution to galaxy evolution. The Kilo Degree Survey (KiDS), i.e. the ESOpublic survey on the VLT Survey Telescope (VST), provides the unprecedentedopportunity to exploit a large galaxy dataset with an exceptional image qualityand depth in the optical wavebands. Using a KiDS subset of about 25,000galaxies with measured spectroscopic redshifts, we have derived photo-z's usingi) three different empirical methods based on supervised machine learning, ii)the Bayesian Photometric Redshift model (or BPZ), and iii) a classical SEDtemplate fitting procedure (Le Phare). We confirm that, in the regions of thephotometric parameter space properly sampled by the spectroscopic templates,machine learning methods provide better redshift estimates, with a lowerscatter and a smaller fraction of outliers. SED fitting techniques, however,provide useful information on the galaxy spectral type which can be effectivelyused to constrain systematic errors and to better characterize potentialcatastrophic outliers. Such classification is then used to specialize thetraining of regression machine learning models, by demonstrating that a hybridapproach, involving SED fitting and machine learning in a single collaborativeframework, can be effectively used to improve the accuracy of photo-zestimates.

A cooperative approach among methods for photometric redshifts estimation: an application to KiDS data