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

M. Bilicki, H. Hoekstra, M. J. I. Brown, V. Amaro, C. Blake, S. Cavuoti, J. T. A. de Jong, C. Georgiou, H. Hildebrandt, C. Wolf, A. Amon, M. Brescia, S. Brough, M. V. Costa-Duarte, T. Erben, K. Glazebrook, A. Grado, C. Heymans, T. Jarrett, S. JoudakiK. Kuijken, G. Longo, N. Napolitano, D. Parkinson, C. Vellucci, G. A. Verdoes Kleijn, L. Wang

Research output: Contribution to journalArticleAcademicpeer-review

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 languageEnglish
Article numberA69
Pages (from-to)1-22
JournalA&A
Volume616
DOIs
Publication statusPublished - Aug 2018

Bibliographical note

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

Keywords

  • astro-ph.CO
  • astro-ph.GA
  • astro-ph.IM

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