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

Bilicki, Maciej Iej; Hoekstra, Henk; Brown, M J I; Amaro, V; Blake, Chris; Cavuoti, Stefano; De Jong, J T A; Georgiou, C; Hildebrandt, H; Wolf, Christian; Amon, Alexandra

Description

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 z(phot) less than or similar to 0.9 and r less than or similar to 23.5. At the bright end of r less than or similar to 20, where very complete spectroscopic data...[Show more]

CollectionsANU Research Publications
Date published: 2018
Type: Journal article
URI: http://hdl.handle.net/1885/207800
Source: Astronomy and Astrophysics (Online)
DOI: 10.1051/0004-6361/201731942
Access Rights: Open Access

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