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

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. JoudakiK. Kuijken, G. Longo, N. Napolitano, D. Parkinson, C. Vellucci, G. A. Verdoes Kleijn, L. Wang

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20 Citations (Scopus)

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 ≲ 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 ≲ 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. ESO 2018.

Original languageEnglish
Article numberA69
Number of pages22
JournalAstronomy & Astrophysics
Volume616
DOIs
Publication statusPublished - 1 Jan 2018

Keywords

  • Catalogs
  • Data analysis
  • Distances and redshifts
  • Galaxies
  • Large-scale structure of Universe
  • Methods
  • Numerical
  • Statistical

Cite this

Bilicki, M., Hoekstra, H., Brown, M. J. I., Amaro, V., Blake, C., Cavuoti, S., De Jong, J. T. A., Georgiou, C., Hildebrandt, H., Wolf, C., Amon, A., Brescia, M., Brough, S., Costa-Duarte, M. V., Erben, T., Glazebrook, K., Grado, A., Heymans, C., Jarrett, T., ... Wang, L. (2018). Photometric redshifts for the Kilo-Degree Survey: Machine-learning analysis with artificial neural networks. Astronomy & Astrophysics, 616, [A69]. https://doi.org/10.1051/0004-6361/201731942