Constraining Source Redshift Distributions with Gravitational Lensing

TL;DR

This paper presents a novel lensing-based method to independently constrain galaxy redshift distributions, offering a systematic check on photometric redshifts that is less affected by spectral misidentification.

Contribution

The authors introduce a new technique using weak gravitational lensing shear around clusters to determine galaxy redshift distributions independently of spectral properties.

Findings

Approximately 40 cluster lenses can determine redshift bin fractions to ~11% accuracy.

Large surveys could achieve 1% precision if lens mass priors are maintained.

The method is complementary, with different systematics compared to traditional photometric redshift techniques.

Abstract

We introduce a new method for constraining the redshift distribution of a set of galaxies, using weak gravitational lensing shear. Instead of using observed shears and redshifts to constrain cosmological parameters, we ask how well the shears around clusters can constrain the redshifts, assuming fixed cosmological parameters. This provides a check on photometric redshifts, independent of source spectral energy distribution properties and therefore free of confounding factors such as misidentification of spectral breaks. We find that ~40 massive ($\sigma_v=1200$ km/s) cluster lenses are sufficient to determine the fraction of sources in each of six coarse redshift bins to ~11%, given weak (20%) priors on the masses of the highest-redshift lenses, tight (5%) priors on the masses of the lowest-redshift lenses, and only modest (20-50%) priors on calibration and evolution effects. Additional massive lenses drive down uncertainties as $N_{lens}^0.5$, but the improvement slows as one is forced to use lenses further down the mass function. Future large surveys contain enough clusters to reach 1% precision in the bin fractions if the tight lens mass priors can be maintained for large samples of lenses. In practice this will be difficult to achieve, but the method may be valuable as a complement to other more precise methods because it is based on different physics and therefore has different systematic errors.