Photometric redshifts: estimating their contamination and distribution using clustering information

TL;DR

This paper introduces a novel method to estimate contamination between photometric redshift bins using clustering information, validated with simulations and applied to real survey data to improve redshift distribution accuracy.

Contribution

The paper develops a theoretical framework for contamination estimation in multiple redshift bins and demonstrates its effectiveness with mock data and real survey application.

Findings

Artificial contamination can be accurately recovered in simulations.

Degeneracies limit unique solutions but constraints improve with larger data.

The method successfully estimates contamination and true redshift distributions in real data.

Abstract

We present a new technique to estimate the level of contamination between photometric redshift bins. If the true angular cross-correlation between redshift bins can be safely assumed to be zero, any measured cross-correlation is a result of contamination between the bins. We present the theory for an arbitrary number of redshift bins, and discuss in detail the case of two and three bins which can be easily solved analytically. We use mock catalogues constructed from the Millennium Simulation to test the method, showing that artificial contamination can be successfully recovered with our method. We find that degeneracies in the parameter space prohibit us from determining a unique solution for the contamination, though constraints are made which can be improved with larger data sets. We then apply the method to an observational galaxy survey: the deep component of the Canada-France-Hawaii Telescope Legacy Survey. We estimate the level of contamination between photometric redshift bins and demonstrate our ability to reconstruct both the true redshift distribution and the true average redshift of galaxies in each photometric bin.