TY - JOUR
T1 - Photometric redshifts for the Kilo-Degree Survey
T2 - Machine-learning analysis with artificial neural networks
AU - Bilicki, M.
AU - Hoekstra, H.
AU - Brown, M. J.I.
AU - Amaro, V.
AU - Blake, C.
AU - Cavuoti, S.
AU - De Jong, J. T.A.
AU - Georgiou, C.
AU - Hildebrandt, H.
AU - Wolf, C.
AU - Amon, A.
AU - Brescia, M.
AU - Brough, S.
AU - Costa-Duarte, M. V.
AU - Erben, T.
AU - Glazebrook, K.
AU - Grado, A.
AU - Heymans, C.
AU - Jarrett, T.
AU - Joudaki, S.
AU - Kuijken, K.
AU - Longo, G.
AU - Napolitano, N.
AU - Parkinson, D.
AU - Vellucci, C.
AU - Verdoes Kleijn, G. A.
AU - Wang, L.
N1 - Publisher Copyright:
© EDP Sciences. All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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 ≲ 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 ≲ 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.
AB - 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 ≲ 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 ≲ 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.
KW - Catalogs
KW - Data analysis
KW - Distances and redshifts
KW - Galaxies
KW - Large-scale structure of Universe
KW - Methods
KW - Methods
KW - Methods
KW - Numerical
KW - Statistical
UR - https://www.scopus.com/pages/publications/85051859213
UR - https://www.scopus.com/pages/publications/85051859213#tab=citedBy
U2 - 10.1051/0004-6361/201731942
DO - 10.1051/0004-6361/201731942
M3 - Article
AN - SCOPUS:85051859213
SN - 0004-6361
VL - 616
JO - Astronomy and Astrophysics
JF - Astronomy and Astrophysics
M1 - A69
ER -