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
Original language | English |
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Article number | A69 |
Pages (from-to) | 1-22 |
Journal | A&A |
Volume | 616 |
DOIs | |
Publication status | Published - Aug 2018 |
Bibliographical note
A&A, in press. Data available from the KiDS website http://kids.strw.leidenuniv.nl/DR3/ml-photoz.php#annz2Keywords
- astro-ph.CO
- astro-ph.GA
- astro-ph.IM