Redshift distributions of galaxies in the DES Science Verification shear catalogue and implications for weak lensing

TL;DR

This paper evaluates photometric redshift methods for DES weak lensing data, constructs tomographic bins, and assesses how redshift uncertainties impact cosmological parameters, finding shifts within statistical errors.

Contribution

It compares multiple photometric redshift techniques, constructs reliable tomographic bins, and quantifies their effect on cosmological parameters in DES weak lensing analysis.

Findings

Photometric redshift uncertainties cause ~3% shift in σ8.

Tomographic bins have systematic uncertainties δz ≲ 0.05.

Bias in critical surface density is within statistical power.

Abstract

We present photometric redshift estimates for galaxies used in the weak lensing analysis of the Dark Energy Survey Science Verification (DES SV) data. Four model- or machine learning-based photometric redshift methods -- ANNZ2, BPZ calibrated against BCC-Ufig simulations, SkyNet, and TPZ -- are analysed. For training, calibration, and testing of these methods, we construct a catalogue of spectroscopically confirmed galaxies matched against DES SV data. The performance of the methods is evaluated against the matched spectroscopic catalogue, focusing on metrics relevant for weak lensing analyses, with additional validation against COSMOS photo-zs. From the galaxies in the DES SV shear catalogue, which have mean redshift $0.72\pm0.01$ over the range $0.3<z<1.3$, we construct three tomographic bins with means of $z=\{0.45, 0.67, 1.00\}$. These bins each have systematic uncertainties $\delta z \lesssim 0.05$ in the mean of the fiducial SkyNet photo-z $n(z)$. We propagate the errors in the redshift distributions through to their impact on cosmological parameters estimated with cosmic shear, and find that they cause shifts in the value of $\sigma_8$ of approx. 3%. This shift is within the one sigma statistical errors on $\sigma_8$ for the DES SV shear catalog. We further study the potential impact of systematic differences on the critical surface density, $\Sigma_{\mathrm{crit}}$, finding levels of bias safely less than the statistical power of DES SV data. We recommend a final Gaussian prior for the photo-z bias in the mean of $n(z)$ of width $0.05$ for each of the three tomographic bins, and show that this is a sufficient bias model for the corresponding cosmology analysis.