Clustering-based redshift estimation: application to VIPERS/CFHTLS

TL;DR

This paper evaluates the clustering-based redshift estimation method using VIPERS and CFHTLS data, demonstrating its ability to accurately reconstruct redshift distributions and extend to fainter galaxy populations.

Contribution

It applies and tests the clustering-based redshift estimation technique on real survey data, showing its effectiveness and potential for future large surveys.

Findings

Accurately reproduces mean color-redshift relations.

Infers redshift distributions for fainter galaxy populations.

Demonstrates method's applicability to real survey data.

Abstract

We explore the accuracy of the clustering-based redshift estimation proposed by M\'enard et al. (2013) when applied to VIPERS and CFHTLS real data. This method enables us to reconstruct redshift distributions from measurement of the angular clus- tering of objects using a set of secure spectroscopic redshifts. We use state of the art spectroscopic measurements with iAB < 22.5 from the VIMOS Public Extragalactic Redshift Survey (VIPERS) as reference population to infer the redshift distribution of galaxies from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) T0007 release. VIPERS provides a nearly representative sample to the flux limit iAB < 22.5 at redshift > 0.5 which allows us to test the accuracy of the clustering-based red- shift distributions. We show that this method enables us to reproduce the true mean color-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 one of reference and we give an estimate of the color-redshift mapping in this case. This last point is of great interest for future large redshift surveys which suffer from the need of a complete faint spectroscopic sample.