A cooperative approach among methods for photometric redshifts estimation: an application to KiDS data. Issue 2 (10th December 2016)

• Record Type: Journal Article
• Authors: Cavuoti, S.; Tortora, C.; Brescia, M.; Longo, G.; Radovich, M.; Napolitano, N. R.; Amaro, V.; Vellucci, C.; La Barbera, F.; Getman, F.; Grado, A.
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 466:Issue 2(2017)
• Page Start: 2039
• Page End: 2053
• Publication Date: 2016-12-10
• Subjects: methods: data analysis -- catalogues; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stw3208
• Languages: English
• ISSNs: 0035-8711

Abstract

Abstract: Photometric redshifts (photo- z ) are fundamental in galaxy surveys to address different topics, from gravitational lensing and dark matter distribution to galaxy evolution. The Kilo Degree Survey (KiDS), i.e. the European Southern Observatory (ESO) public survey on the VLT Survey Telescope (VST), provides the unprecedented opportunity to exploit a large galaxy data set with an exceptional image quality and depth in the optical wavebands. Using a KiDS subset of about 25000 galaxies with measured spectroscopic redshifts, we have derived photo- z using (i) three different empirical methods based on supervised machine learning; (ii) the Bayesian photometric redshift model (or BPZ); and (iii) a classical spectral energy distribution (SED) template fitting procedure (le phare ). We confirm that, in the regions of the photometric parameter space properly sampled by the spectroscopic templates, machine learning methods provide better redshift estimates, with a lower scatter and a smaller fraction of outliers. SED fitting techniques, however, provide useful information on the galaxy spectral type, which can be effectively used to constrain systematic errors and to better characterize potential catastrophic outliers. Such classification is then used to specialize the training of regression machine learning models, by demonstrating that a hybrid approach, involving SED fitting and machine learning in a single collaborative framework, can be effectively used to improve the accuracy of photo- z estimates.