Machine Learning based photometric redshifts for the KiDS ESO DR2 galaxies

Stefano Cavuoti; Massimo Brescia; Crescenzo Tortora; Giuseppe Longo; Nicola R. Napolitano; Mario Radovich; Francesco La Barbera; Massimo Capaccioli; Jelte T. A. de Jong; Fedor Getman; Aniello Grado; Maurizio Paolillo

doi:10.1093/mnras/stv1496

arXiv · 1507.00754

Machine Learning based photometric redshifts for the KiDS ESO DR2 galaxies

Stefano Cavuoti·Massimo Brescia·Crescenzo Tortora·Giuseppe Longo·Nicola R. Napolitano·Mario Radovich·Francesco La Barbera·Massimo Capaccioli·Jelte T. A. de Jong·Fedor Getman·Aniello Grado·Maurizio Paolillo

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Abstract

We estimated photometric redshifts (zphot) for more than 1.1 million galaxies of the ESO Public Kilo-Degree Survey (KiDS) Data Release 2. KiDS is an optical wide-field imaging survey carried out with the VLT Survey Telescope (VST) and the OmegaCAM camera, which aims at tackling open questions in cosmology and galaxy evolution, such as the origin of dark energy and the channel of galaxy mass growth. We present a catalogue of photometric redshifts obtained using the Multi Layer Perceptron with Quasi Newton Algorithm (MLPQNA) model, provided within the framework of the DAta Mining and Exploration Web Application REsource (DAMEWARE). These photometric redshifts are based on a spectroscopic knowledge base which was obtained by merging spectroscopic datasets from GAMA (Galaxy And Mass Assembly) data release 2 and SDSS-III data release 9. The overall 1 sigma uncertainty on Delta z = (zspec - zphot) / (1+ zspec) is ~ 0.03, with a very small average bias of ~ 0.001, a NMAD of ~ 0.02 and a fraction of catastrophic outliers (| Delta z | > 0.15) of ~0.4%.

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Stefano Cavuoti, Massimo Brescia, Crescenzo Tortora, Giuseppe Longo, Nicola R. Napolitano, Mario Radovich, Francesco La Barbera, Massimo Capaccioli, Jelte T. A. de Jong, Fedor Getman, Aniello Grado, Maurizio Paolillo. 2015-07-30. Machine Learning based photometric redshifts for the KiDS ESO DR2 galaxies. https://doi.org/10.1093/mnras%2Fstv1496

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