Clustering-based redshift estimation: application to VIPERS/CFHTLS

V. Scottez, Yannick Mellier, B. R. Granett, T. Moutard, Martin Kilbinger, M. Scodeggio, Bianca Garilli, M. Bolzonella, Sylvain de la Torre, Luigi Guzzo, Ummi Abbas, C. Adami, S. Arnouts, D. Bottini, Enzo Branchini, A. Cappi, O. Cucciati, I. Davidzon, A. Fritz, Paolo FranzettiA. Iovino, J. Krywult, V. Le Brun, O. Le Fèvre, D. Maccagni, K. Małek, Federico Marulli, M. Polletta, A. Pollo, L. A. M. Tasca, R. Tojeiro, D. Vergani, A. Zanichelli, J. Bel, J. Coupon, G. De Lucia, O. Ilbert, H. J. McCracken, Lauro Moscardini

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We explore the accuracy of the clustering-based redshift estimation proposed by Ménard et al. when applied to VIMOS Public Extragalactic Redshift Survey (VIPERS) and Canada–France–Hawaii Telescope Legacy Survey (CFHTLS) real data. This method enables us to reconstruct redshift distributions from measurement of the angular clustering of objects using a set of secure spectroscopic redshifts. We use state-of-the-art spectroscopic measurements with iAB < 22.5 from the VIPERS as reference population to infer the redshift distribution of galaxies from the CFHTLS T0007 release. VIPERS provides a nearly representative sample to a flux limit of iAB < 22.5 at a redshift of >0.5 which allows us to test the accuracy of the clustering-based redshift distributions. We show that this method enables us to reproduce the true mean colour–redshift relation when both populations have the same magnitude limit. We also show that this technique allows the inference of redshift distributions for a population fainter than the reference and we give an estimate of the colour–redshift mapping in this case. This last point is of great interest for future large-redshift surveys which require a complete faint spectroscopic sample.
Original languageEnglish
Pages (from-to)1682-1696
Number of pages15
JournalMonthly Notices of the Royal Astronomical Society
Issue number2
Early online date8 Jul 2016
Publication statusPublished - 21 Oct 2016


  • astro-ph.CO
  • astro-ph.GA
  • astro-ph.IM
  • methods: data analysis
  • surveys
  • clustering
  • galaxies: distances and redshifts


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