Photometric redshifts for the Kilo-Degree Survey

Machine-learning analysis with artificial neural networks

¹ Leiden Observatory, Leiden University, PO Box 9513, 2300 RA Leiden, The Netherlands
e-mail bilicki@strw.leidenuniv.nl
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

Received: 13 September 2017
Accepted: 30 April 2018

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 to z_phot ≲ 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 σ_δz/(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 σ_δz/(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.