Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts - Methods and Systematics Characterization

TL;DR

This paper evaluates a clustering-based method using simulations to calibrate photometric redshift biases in the Dark Energy Survey Year 1 data, highlighting dominant systematic uncertainties affecting redshift calibration accuracy.

Contribution

It introduces a systematic characterization of a clustering-based redshift calibration method and assesses its uncertainties using simulated DES Y1 data.

Findings

Systematic uncertainties dominate the calibration error budget.

The mean redshift bias uncertainty is approximately 0.02.

Differences between clustering-derived and photo-$z$ distributions impact calibration.

Abstract

We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing (WL) source galaxies from the Dark Energy Survey Year 1 (DES Y1) sample with redMaGiC galaxies (luminous red galaxies with secure photometric redshifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-$z$ methods applied to the same source galaxy sample. We apply the method to three photo-$z$ codes run in our simulated data: Bayesian Photometric Redshift (BPZ), Directional Neighborhood Fitting (DNF), and Random Forest-based photo-$z$ (RF). We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering vs photo-$z$'s. The systematic uncertainty in the mean redshift bias of the source galaxy sample is $\Delta z \lesssim 0.02$, though the precise value depends on the redshift bin under consideration. We discuss possible ways to mitigate the impact of our dominant systematics in future analyses.