TY - JOUR
T1 - Photometric redshifts for the Kilo-Degree Survey. 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.
PY - 2018/8/1
Y1 - 2018/8/1
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 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.
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 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.
KW - galaxies: distances and redshifts
KW - catalogs
KW - large-scale structure of Universe
KW - methods: data analysis
KW - methods: numerical
KW - methods: statistical
KW - Astrophysics - Cosmology and Nongalactic Astrophysics
KW - Astrophysics - Astrophysics of Galaxies
KW - Astrophysics - Instrumentation and Methods for Astrophysics
U2 - 10.1051/0004-6361/201731942
DO - 10.1051/0004-6361/201731942
M3 - Article
VL - 616
JO - Astronomy & astrophysics
JF - Astronomy & astrophysics
SN - 0004-6361
IS - August 2018
M1 - A69
ER -