Volume 616, August 2018
|Number of page(s)||22|
|Section||Catalogs and data|
|Published online||21 August 2018|
Photometric redshifts for the Kilo-Degree Survey
Machine-learning analysis with artificial neural networks
Leiden Observatory, Leiden University, PO Box 9513, 2300 RA Leiden, The Netherlands
2 National Centre for Nuclear Research, Astrophysics Division, PO Box 447, 90-950 Łódź, Poland
3 Janusz Gil Institute of Astronomy, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland
4 School of Physics and Astronomy, Monash University, Clayton, VIC 3800, Australia
5 Department of Physics “E. Pancini”, University Federico II, Via Cinthia 6, 80126 Napoli, Italy
6 Centre for Astrophysics & Supercomputing, Swinburne University of Technology, PO Box 218, Hawthorn, VIC 3122, Australia
7 INAF – Astronomical Observatory of Capodimonte, Via Moiariello 16, 80131 Napoli, Italy
8 INFN – Section of Naples, Via Cinthia 6, 80126 Napoli, Italy
9 Kapteyn Astronomical Institute, University of Groningen, Postbus 800, 9700 AV Groningen, The Netherlands
10 Argelander-Institut für Astronomie, Auf dem Hügel 71, 53121 Bonn, Germany
11 Research School of Astronomy and Astrophysics, Australian National University, Canberra, ACT 2611, Australia
12 School of Physics, University of New South Wales, NSW 2052, Australia
13 Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, R. do Matão 1226, 05508-090 São Paulo, Brazil
14 Scottish Universities Physics Alliance, Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK
15 Department of Astronomy, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa
16 Department of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK
17 School of Mathematics and Physics, University of Queensland, Brisbane, QLD 4072, Australia
18 Korea Astronomy and Space Science Institute, Daejeon 34055, Korea
19 SRON Netherlands Institute for Space Research, Landleven 12, 9747 AD Groningen, The Netherlands
Accepted: 30 April 2018
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 to zphot ≲ 0.9 and r ≲ 23.5. At the bright end of r ≲ 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-z method 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-z derivation, as well as when VIKING and WISE infrared (IR) bands are added. While the fiducial four-band ugri setup gives a photo-z bias 〈δz/(1 + z)〉 = −2 × 10−4 and scatter σδz/(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 the ugri and IR magnitudes are joined into 12-band photometry spanning up to 12 μm, 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 × 10−5 and σδz/(1+z) < 0.019. This paper also serves as a reference for two public photo-z catalogues accompanying KiDS DR3, both obtained using the ANNz2 code. The first one, of general purpose, includes all the 39 million KiDS sources with four-band ugri measurements in DR3. The second dataset, optimised for low-redshift studies such as galaxy-galaxy lensing, is limited to r ≲ 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-z derivation.
Key words: galaxies: distances and redshifts / catalogs / large-scale structure of Universe / methods: data analysis / methods: numerical / methods: statistical
© ESO 2018
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