Year: 2018
Type: article
Abstract: We present a machine-learning photometric redshift (ML photo-z) analysis of the Kilo-Degree Survey Data Release 3 (KiDS DR3), using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets, these ML codes provide photo-zs of quality comparable to, if not better than, those from the Bayesian Photometric Redshift (BPZ) code, at least up tozphot≲ 0.9 andr≲ 23.5. At the bright end ofr≲ 20, where very complete spectroscopic data overlapping with KiDS are available, the performance of the ML photo-zs clearly surpasses that of BPZ, currently the primary photo-zmethod for KiDS. Using the Galaxy And Mass Assembly (GAMA) spectroscopic survey as calibration, we furthermore study how photo-zs improve for bright sources when photometric parameters additional to magnitudes are included in the photo-zderivation, as well as when VIKING and WISE infrared (IR) bands are added. While the fiducial four-bandugrisetup gives a photo-zbias 〈δz/(1 +z)〉 = −2 × 10−4and scatterσδz/(1+z)< 0.022 at mean 〈z〉 = 0.23, combining magnitudes, colours, and galaxy sizes reduces the scatter by ~7% and the bias by an order of magnitude. Once theugriand IR magnitudes are joined into 12-band photometry spanning up to 12μm, the scatter decreases by more than 10% over the fiducial case. Finally, using the 12 bands together with optical colours and linear sizes gives 〈δz/(1 +z)〉 < 4 × 10−5andσδz/(1+z)< 0.019. This paper also serves as a reference for two public photo-zcatalogues accompanying KiDS DR3, both obtained using the ANNz2 code. The first one, of general purpose, includes all the 39 million KiDS sources with four-bandugrimeasurements in DR3. The second dataset, optimised for low-redshift studies such as galaxy-galaxy lensing, is limited tor≲ 20, and provides photo-zs of much better quality than in the full-depth case thanks to incorporating optical magnitudes, colours, and sizes in the GAMA-calibrated photo-zderivation.
Source: Astronomy and Astrophysics
Authors: M. Bilicki, H. Hoekstra, M. J. I. Brown, V. Amaro, C. Blake, S. Cavuoti, J. T. A. de Jong, C. Georgiou, H. Hildebrandt, C. Wolf, A. Amon, M. Brescia, S. Brough, M. V. Costa-Duarte, T. Erben, K. Glazebrook, A. Grado, C. Heymans, T. Jarrett, S. Joudaki, K. Kuijken, G. Longo, N. Napolitano, D. Parkinson, C. Vellucci, G. A. Verdoes Kleijn, L. Wang
Institutions: Leiden University, National Centre for Nuclear Research, Leiden Observatory, University of Zielona Góra, Monash University, University of Naples Federico II, Swinburne University of Technology, Astronomical Observatory of Capodimonte, Istituto Nazionale di Fisica Nucleare, Sezione di Napoli, University of Groningen, University of Bonn, Australian National University, Royal Observatory, Scottish Universities Physics Alliance, University of Edinburgh, UNSW Sydney, Universidade de São Paulo, University of Cape Town, University of Oxford, The University of Queensland, Korea Astronomy and Space Science Institute, Space Research Organisation Netherlands
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Funders: National Science Foundation, U.S. Department of Energy, Alfred P. Sloan Foundation, Alexander von Humboldt-Stiftung, Yale University, Vanderbilt University, Princeton University, Ohio State University, Harvard University, University of Washington, Johns Hopkins University, University of Arizona, Carnegie Mellon University, University of Portsmouth, New Mexico State University, York University, Deutsche Forschungsgemeinschaft, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Università degli Studi di Padova, Narodowe Centrum Nauki, Office of Science, College of Engineering, Michigan State University, Science and Technology Facilities Council, European Research Council, Australian Research Council, Australian Astronomical Optics-Macquarie, INAF-Osservatorio Astronomico di Padova, Brookhaven National Laboratory, Lawrence Berkeley National Laboratory, H2020 European Research Council
OpenAlex ID: W4289726834