Recovering Redshift Distributions with Cross-Correlations: Pushing The Boundaries

TL;DR

This paper enhances cross-correlation methods for redshift distribution recovery by incorporating small scale clustering data, demonstrating reliable results even with non-linear clustering assumptions and proposing bias mitigation techniques.

Contribution

It introduces an approach to include small scale clustering in redshift recovery, addressing galaxy bias challenges and improving accuracy for narrow redshift distributions.

Findings

Reliable redshift recovery with non-linear clustering assumptions.

Intermediate scale information balances data richness and bias.

Tomographic binning improves redshift estimates.

Abstract

Determining accurate redshift distributions for very large samples of objects has become increasingly important in cosmology. We investigate the impact of extending cross-correlation based redshift distribution recovery methods to include small scale clustering information. The major concern in such work is the ability to disentangle the amplitude of the underlying redshift distribution from the influence of evolving galaxy bias. Using multiple simulations covering a variety of galaxy bias evolution scenarios, we demonstrate reliable redshift recoveries using linear clustering assumptions well into the non-linear regime for redshift distributions of narrow redshift width. Including information from intermediate physical scales balances the increased information available from clustering and the residual bias incurred from relaxing of linear constraints. We discuss how breaking a broad sample into tomographic bins can improve estimates of the redshift distribution, and present a simple bias removal technique using clustering information from the spectroscopic sample alone.