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dc.contributor.authorScottez, V.
dc.contributor.authorMellier, Y.
dc.contributor.authorGranett, B. R.
dc.contributor.authorMoutard, T.
dc.contributor.authorKilbinger, M.
dc.contributor.authorScodeggio, M.
dc.contributor.authorGarilli, B.
dc.contributor.authorBolzonella, M.
dc.contributor.authorde la Torre, S.
dc.contributor.authorGuzzo, L.
dc.contributor.authorAbbas, U.
dc.contributor.authorAdami, C.
dc.contributor.authorArnouts, S.
dc.contributor.authorBottini, D.
dc.contributor.authorBranchini, E.
dc.contributor.authorCappi, A.
dc.contributor.authorCucciati, O.
dc.contributor.authorDavidzon, I.
dc.contributor.authorFritz, A.
dc.contributor.authorFranzetti, P.
dc.contributor.authorIovino, A.
dc.contributor.authorKrywult, J.
dc.contributor.authorLe Brun, V.
dc.contributor.authorLe Fèvre, O.
dc.contributor.authorMaccagni, D.
dc.contributor.authorMałek, K.
dc.contributor.authorMarulli, F.
dc.contributor.authorPolletta, M.
dc.contributor.authorPollo, A.
dc.contributor.authorTasca, L. A. M.
dc.contributor.authorTojeiro, R.
dc.contributor.authorVergani, D.
dc.contributor.authorZanichelli, A.
dc.contributor.authorBel, J.
dc.contributor.authorCoupon, J.
dc.contributor.authorDe Lucia, G.
dc.contributor.authorIlbert, O.
dc.contributor.authorMcCracken, H. J.
dc.contributor.authorMoscardini, L.
dc.date.accessioned2017-03-31T09:30:16Z
dc.date.available2017-03-31T09:30:16Z
dc.date.issued2016-07-08
dc.identifier.citationScottez , V , Mellier , Y , Granett , B R , Moutard , T , Kilbinger , M , Scodeggio , M , Garilli , B , Bolzonella , M , de la Torre , S , Guzzo , L , Abbas , U , Adami , C , Arnouts , S , Bottini , D , Branchini , E , Cappi , A , Cucciati , O , Davidzon , I , Fritz , A , Franzetti , P , Iovino , A , Krywult , J , Le Brun , V , Le Fèvre , O , Maccagni , D , Małek , K , Marulli , F , Polletta , M , Pollo , A , Tasca , L A M , Tojeiro , R , Vergani , D , Zanichelli , A , Bel , J , Coupon , J , De Lucia , G , Ilbert , O , McCracken , H J & Moscardini , L 2016 , ' Clustering-based redshift estimation : application to VIPERS/CFHTLS ' , Monthly Notices of the Royal Astronomical Society , vol. 462 , no. 2 , pp. 1683-1696 . https://doi.org/10.1093/mnras/stw1500en
dc.identifier.issn0035-8711
dc.identifier.otherPURE: 249426995
dc.identifier.otherPURE UUID: d8f38c49-6971-4c37-8576-08df0192013a
dc.identifier.otherBibCode: 2016MNRAS.462.1683S
dc.identifier.otherScopus: 84988884567
dc.identifier.urihttps://hdl.handle.net/10023/10555
dc.description.abstractWe 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.
dc.format.extent14
dc.language.isoeng
dc.relation.ispartofMonthly Notices of the Royal Astronomical Societyen
dc.rights© 2016 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. This work is made available online in accordance with the publisher’s policies. This is the final published version of the work, which was originally published at: https://dx.doi.org/10.1093/mnras/stw1500en
dc.subjectMethods: data analysisen
dc.subjectSurveysen
dc.subjectClusteringen
dc.subjectGalaxies: distances and redshiftsen
dc.subjectQB Astronomyen
dc.subjectQC Physicsen
dc.subject3rd-DASen
dc.subject.lccQBen
dc.subject.lccQCen
dc.titleClustering-based redshift estimation : application to VIPERS/CFHTLSen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Physics and Astronomyen
dc.identifier.doihttps://doi.org/10.1093/mnras/stw1500
dc.description.statusPeer revieweden
dc.identifier.urlhttps://arxiv.org/abs/1605.05501en
dc.identifier.urlhttp://adsabs.harvard.edu/abs/2016MNRAS.462.1683Sen


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