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.; Jong, J. T. A. de; 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. Verdoes; Wang, L.

doi:https://doi.org/10.1051/0004-6361/201731942

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

DSpace/Manakin Repository

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.; Jong, J. T. A. de; 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. Verdoes; Wang, L.

(2018) A&A, volume 616, pp. 1 - 22

(Article)

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 ... read more

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Keywords: astro-ph.CO, astro-ph.GA, astro-ph.IM, Taverne

DOI: https://doi.org/10.1051/0004-6361/201731942

ISSN: 1406-345X

Note: A&A, in press. Data available from the KiDS website http://kids.strw.leidenuniv.nl/DR3/ml-photoz.php#annz2

(Peer reviewed)

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