Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks
Publication date
2018-08
Authors
Bilicki, M.
Hoekstra, H.
Brown, M. J. I.
Amaro, V.
Blake, C.
Cavuoti, S.
Jong, J. T. A. de
Georgiou, Christos
Hildebrandt, H.
Wolf, C.
Editors
Advisors
Supervisors
Document Type
Article
Metadata
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Abstract
We present a machine-learning photometric redshift analysis of the Kilo-Degree Survey Data Release 3, 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 BPZ code, at least up to zphot
Keywords
astro-ph.CO, astro-ph.GA, astro-ph.IM, Taverne
Citation
Bilicki, M, Hoekstra, H, Brown, M J I, Amaro, V, Blake, C, Cavuoti, S, Jong, J T A D, 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, Kleijn, G A V & Wang, L 2018, 'Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks', A&A, vol. 616, A69, pp. 1-22. https://doi.org/10.1051/0004-6361/201731942