2dFLenS and KiDS: Determining source redshift distributions with cross-correlations
Abstract
We develop a statistical estimator to infer the redshift probability distribution of a photometric sample of galaxies from its angular cross-correlation in redshift bins with an overlapping spectroscopic sample. This estimator is a minimum variance weighted quadratic function of the data: a quadratic estimator. This extends and modifies the methodology presented by McQuinn & White (2013). The derived source redshift distribution is degenerate with the source galaxy bias, which must be constrained via additional assumptions. We apply this estimator to constrain source galaxy redshift distributions in the Kilo-Degree imaging survey through cross-correlation with the spectroscopic 2-degree Field Lensing Survey, presenting results first as a binned step-wise distribution in the range z < 0.8, and then building a continuous distribution using a Gaussian process model. We demonstrate the robustness of our methodology using mock catalogues constructed from N-body simulations, and comparisons with other techniques for inferring the redshift distribution.
Keywords
Cite
@article{arxiv.1611.07578,
title = {2dFLenS and KiDS: Determining source redshift distributions with cross-correlations},
author = {Andrew Johnson and Chris Blake and Alexandra Amon and Thomas Erben and Karl Glazebrook and Joachim Harnois-Deraps and Catherine Heymans and Hendrik Hildebrandt and Shahab Joudaki and Dominik Klaes and Konrad Kuijken and Chris Lidman and Felipe A. Marin and John McFarland and Christopher B. Morrison and David Parkinson and Gregory B. Poole and Mario Radovich and Christian Wolf},
journal= {arXiv preprint arXiv:1611.07578},
year = {2017}
}
Comments
17 pages, 10 figures, accepted for publication by MNRAS