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

Photometric redshifts (photo-z's) 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 ESO public survey on the VLT Survey Telescope (VST), provides the unprecedented o...

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Bibliographic Details
Published in:arXiv.org 2016-12
Main Authors: Cavuoti, Stefano, Tortora, Crescenzo, Brescia, Massimo, Longo, Giuseppe, Radovich, Mario, Napolitano, Nicola R, Amaro, Valeria, Vellucci, Civita, Francesco La Barbera, Getman, Fedor, Grado, Aniello
Format: Article
Language:English
Subjects:
Artificial intelligence
Bayesian analysis
Dark matter
European Southern Observatory
Galactic evolution
Galaxies
Galaxy distribution
Gravitational lenses
Image quality
Machine learning
Outliers (statistics)
Photometry
Red shift
Systematic errors
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Summary:Photometric redshifts (photo-z's) 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 ESO public survey on the VLT Survey Telescope (VST), provides the unprecedented opportunity to exploit a large galaxy dataset with an exceptional image quality and depth in the optical wavebands. Using a KiDS subset of about 25,000 galaxies with measured spectroscopic redshifts, we have derived photo-z's using i) three different empirical methods based on supervised machine learning, ii) the Bayesian Photometric Redshift model (or BPZ), and iii) a classical 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.
ISSN:2331-8422
DOI:10.48550/arxiv.1612.02173