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

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.; Joudaki, S.; Kuijken, K.; Longo, G.; Napolitano, N.; Parkinson, D.; Vellucci, C.; Verdoes Kleijn, G. A.; Wang, L.

doi:10.1051/0004-6361/201731942

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. © EDP Sciences. All rights reserved.

Titolo:	Photometric redshifts for the Kilo-Degree Survey: Machine-learning analysis with artificial neural networks
Autori:	Bilicki, M. Hoekstra, H. Brown, M. J. I. AMARO, VALERIA Blake, C. CAVUOTI, STEFANO 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. Joudaki, S. Kuijken, K. LONGO, GIUSEPPE Napolitano, N. Parkinson, D. Vellucci, C. Verdoes Kleijn, G. A. Wang, L. mostra contributor esterni nascondi contributor esterni
Data di pubblicazione:	2018
Rivista:	ASTRONOMY & ASTROPHYSICS
Abstract:	We present a machine-learning photometric redshift (ML photo-z) analysis of the Kilo-Degree Surve...y 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. © EDP Sciences. All rights reserved.
Handle:	http://hdl.handle.net/11588/741620
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