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In the modern galaxy surveys photometric redshifts play a central role in abroad range of studies, from gravitational lensing and dark matter distributionto galaxy evolution. Using a dataset of about 25,000 galaxies from the seconddata release of the Kilo Degree Survey (KiDS) we obtain photometric redshiftswith five different methods: (i) Random forest, (ii) Multi Layer Perceptronwith Quasi Newton Algorithm, (iii) Multi Layer Perceptron with an optimizationnetwork based on the Levenberg-Marquardt learning rule, (iv) the BayesianPhotometric Redshift model (or BPZ) and (v) a classical SED template fittingprocedure (Le Phare). We show how SED fitting techniques could provide usefulinformation on the galaxy spectral type which can be used to improve thecapability of machine learning methods constraining systematic errors andreduce the occurrence of catastrophic outliers. We use such classification totrain specialized regression estimators, by demonstrating that such hybridapproach, involving SED fitting and machine learning in a single collaborativeframework, is capable to improve the overall prediction accuracy of photometricredshifts.

Cooperative photometric redshift estimation