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

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...

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Bibliographic Details
Main Authors:	Bilicki, M, Hoekstra, H, Brown, M, Amaro, V, Blake, C, Cavuoti, S, De Jong, J, Georgiou, C, Hildebrandt, H, Wolf, C, Amon, A, Brescia, M, Brough, S, Costa-Duarte, M, Erben, T, Glazebrook, K, Grado, A, Heymans, C, Jarrett, T, Joudaki, S, Kuijken, K, Longo, G, Napolitano, N, Parkinson, D, Vellucci, C, Kleijn, G, Wang, L
Format:	Journal article
Published:	EDP Sciences 2018

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

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