Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks

Journal
Date Issued
2018
Author(s)
Bilicki, M.
•
Hoekstra, H.
•
Brown, M. J. I.
•
Amaro, V.
•
Blake, C.
•
•
de Jong, J. T. A.
•
Georgiou, C.
•
Hildebrandt, H.
•
Wolf, C.
•
Amon, A.
•
•
Brough, S.
•
Costa-Duarte, M. V.
•
Erben, T.
•
Glazebrook, K.
•
•
Heymans, C.
•
Jarrett, T.
•
Joudaki, S.
•
Kuijken, K.
•
Longo, G.
•
•
Parkinson, D.
•
Vellucci, C.
•
Verdoes Kleijn, G. A.
•
Wang, L.
DOI
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 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 = 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.
Volume
616
Start page
A69
Uri
Url
Issn Identifier
0004-6361
Ads BibCode
2018A&A...616A..69B
Rights
open.access
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