Dark Energy Survey Year 6 Results: Redshift Calibration of the Weak Lensing Source Galaxies

TL;DR

This work tackles the challenge of calibrating the redshift distributions of DES Y6 weak-lensing source galaxies, a critical component for accurate cosmology with cosmic shear. It develops a joint pipeline that combines SOMPZ (enhanced by SOMF and deep-field Balrog transfer functions) with clustering redshift information (WZ) and image-simulation blending corrections, followed by mode projection to keep cosmologically relevant variations. The resulting ensemble of $n(z)$ realizations, with quantified uncertainties and validated by shear-ratio tests, yields robust mean redshift estimates for four tomographic bins and improves constraints on $\Omega_m$ and $S_8$ relative to previous DES analyses. This methodology and its validation have important implications for upcoming Stage IV surveys, highlighting the need for deep spectroscopic calibration and advanced redshift-sampling techniques to meet stringent cosmological requirements.

Abstract

Determining the distribution of redshifts for galaxies in wide-field photometric surveys is essential for robust cosmological studies of weak gravitational lensing. We present the methodology, calibrated redshift distributions, and uncertainties of the final Dark Energy Survey Year 6 (Y6) weak lensing galaxy data, divided into four redshift bins centered at $\langle z \rangle = [0.414, 0.538, 0.846, 1.157]$. We combine independent information from two methods on the full shape of redshift distributions: optical and near-infrared photometry within an improved Self-Organizing Map $p(z)$ (SOMPZ) framework, and cross-correlations with spectroscopic galaxy clustering measurements (WZ), which we demonstrate to be consistent both in terms of the redshift calibration itself and in terms of resulting cosmological constraints within 0.1$σ$. We describe the process used to produce an ensemble of redshift distributions that account for several known sources of uncertainty. Among these, imperfection in the calibration sample due to the lack of faint, representative spectra is the dominant factor. The final uncertainty on mean redshift in each bin is $σ_{\langle z\rangle} = [0.012, 0.008,0.009, 0.024]$. We ensure the robustness of the redshift distributions by leveraging new image simulations and a cross-check with galaxy shape information via the shear ratio (SR) method.

Figures (18)

• Figure 1: Schematic overview of the DES Y6 source redshift calibration pipeline. The goal is to calibrate the redshift distribution of our target DES Y6 weak lensing source sample. Photometric measurements from the target sample are used within SOMPZ with the redshift sample, the multi-band deep field sample, and the Balrog simulation linking the deep and wide field, to calibrate $n^{\rm{SOMPZ}}(z)$. Sampling over redshift uncertainties generates multiple realizations of this distribution ${n^{\rm{SOMPZ}}(z)}$, which are then constrained through importance sampling using spatial correlation with the reference sample (WZ). Blending effects are corrected using the simulated target sample, and the resulting realizations are projected onto modes using a cosmology-sensitive principal component analysis (PCA) method, before entering cosmological inference. We finally cross-check using the target sample shape information in the SR consistency test.
• Figure 2: The four DES optical+NIR deep fields used for the SOMPZ redshift analysis, with joint photometry from deep DES $ugriz$ and VIDEO/UltraVISTA ${JHK_s}$ bands (red). The overlapping redshift calibration sample is shown from narrow-band COSMOS2020 (yellow) and PAUS+COSMOS (orange), as well as spectroscopy from C3R2, zCOSMOS, VVDS and VIPERS (green to blue). The holes in the image are due to masking of artifacts. The total area and galaxy number after masking are shown numerically in Table \ref{['tab:data_table']}.
• Figure 3: The distribution of redshift calibration data that informs each redshift bin, shown as a function of redshift (top panel) and DES $i-$band magnitude (bottom panel). Each galaxy in this histogram is weighted by the combination of the shear response and statistical weight, and the Balrog injection count. The bar charts (inset) show the relative contributions from the different redshift calibration samples, where we preferentially use spectroscopic redshifts (green), then consider photometric narrow-band PAUS (yellow) and COSMOS2020 (orange), to ensure completeness. The calibration of the high redshift bin, comprised of the most faint galaxies, is dominated by photometric narrow-band data.
• Figure 4: The deep ($c$, top panels) and wide ($\hat{c}$, bottom panels) field Self-Organizing Maps. The SOMs are coloured to represent the mean redshift of each cell (left), the standard deviation of the redshift distribution of each cell scaled by $(1+\langle z \rangle)$ (second), the weighted total number of calibration redshifts galaxies assigned to each SOM cell (third, top), the mean $i$-band magnitude (third, bottom) and the weighted total number of deep/wide DES galaxies assigned to each SOM (right). White cells in the deep SOM indicate parts of colour space for which there are no representative calibration sample galaxies, and the deep galaxies in those cells are discarded from the analysis.
• Figure 5: The relative contribution of each source of uncertainty for the four redshift bins. The variance due to the calibration redshift sample (red) is dominant, followed by the sample variance (green). Compared to these two, the variance due to photometric calibration (yellow) and shot noise (blue) are small. The dashed black lines show the constraints on mean redshift after adding clustering information. The variance of mean redshift is shown in the plot to illustrate the relative magnitude of each source of uncertainty in each bin and their evolution with redshift; the corresponding uncertainties are reported in Table \ref{['tab:uncertainty']}.