1701.08120
Cooperative photometric redshift estimation
Cavuoti et al
In the modern galaxy surveys photometric redshifts play a role in a broad range of studies, from gravitational lensing and DM distribution to galaxy evolution. Using a dataset of about 25,000 galaxies from KiDS-DR2, obtain photo-zs with 5 different methods (i) random forest, (ii) multi layer perception with quasi newton algorithm, (iii) multi layer perception with an optimization network based on the Levenberg-Marquardt learning rule, (iv) the Bayesian Photometric redshift model (BPZ) and (v) a classical SED template fitting procedure (Le Phare). Show how SED fitting techniques could provide useful information on the galaxy spectral type which can be used to improve the capability of machine learning methods constraining systematic errors and reduce the occurrence of catastrophic outliers. Use such classification to train specialized regression estimators, by demonstrating that such hybrid approach, involving SED fitting and machine learning in a single collaborative framework, is capable to improve the overall prediction accuracy of photometric redshifts.
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