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

Authors :
Maciej Bilicki
V. Amaro
Stefano Cavuoti
M. V. Costa-Duarte
Giuseppe Longo
Civita Vellucci
Shahab Joudaki
J. T. A. de Jong
A. Grado
Christos Georgiou
Henk Hoekstra
Thomas Erben
Konrad Kuijken
Sarah Brough
Thomas H. Jarrett
Karl Glazebrook
Nicola R. Napolitano
Lingyu Wang
David Parkinson
Massimo Brescia
Michael J. I. Brown
Christian Wolf
G. A. Verdoes Kleijn
Alexandra Amon
Chris Blake
Hendrik Hildebrandt
Catherine Heymans
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.
Sub Overig UiLOTS
Sub String Theory Cosmology and ElemPart
LS Pharma
Theoretical Physics
ITA
GBR
FRA
DEU
NLD
Astronomy
Source :
Astronomy & Astrophysics, 616, A69, A&A, 616, 1, Astronomy & astrophysics, 616(August 2018):A69. EDP Sciences, Astronomy & Astrophysics
Publication Year :
2018

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

Details

Language :
English
ISSN :
00046361 and 1406345X
Volume :
616
Database :
OpenAIRE
Journal :
Astronomy & astrophysics
Accession number :
edsair.doi.dedup.....71f1bae3a9d0aa8ed8db020d08b5aeb4