We introduce redMaGiC, an automated algorithm for selecting Luminous Red Galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the color-cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine-learning based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalog sampling the redshift range z ∈ [0.2,0.8].Our fiducial sample has a comoving space density of 10 −3 (h −1 Mpc) −3 , and a median photo-z bias (z spec − z photo ) and scatter (σ z /(1 + z)) of 0.005 and 0.017 respectively. The corresponding 5σ outlier fraction is 1.4%. We also test our algorithm with Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8) and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1% level.

We describe the creation, content, and validation of the Dark Energy Survey (DES) internal year-one cosmology data set, Y1A1 GOLD, in support of upcoming cosmological analyses. The Y1A1 GOLD data set is assembled from multiple epochs of DES imaging and consists of calibrated photometric zero-points, object catalogs, and ancillary data products-e.g., maps of survey depth and observing conditions, star-galaxy classification, and photometric redshift estimates-that are necessary for accurate cosmological analyses. The Y1A1 GOLD widearea object catalog consists of~137 million objects detected in co-added images covering~1800 deg 2 in the DES grizY filters. The 10σ limiting magnitude for galaxies is = g 23.4, = r 23.2, = i 22.5, = z 21.8, and = Y 20.1. Photometric calibration of Y1A1 GOLD was performed by combining nightly zero-point solutions with stellar locus regression, and the absolute calibration accuracy is better than 2% over the survey area. DES Y1A1 GOLD is the largest photometric data set at the achieved depth to date, enabling precise measurements of cosmic acceleration at z1.

We measure the cross-correlation between the galaxy density in the Dark Energy Survey (DES) Science Verification data and the lensing of the cosmic microwave background (CMB) as reconstructed with the Planck satellite and the South Pole Telescope (SPT). When using the DES main galaxy sample over the full redshift range 0.2 < z phot < 1.2, a cross-correlation signal is detected at 6σ and 4σ with SPT and Planck respectively. We then divide the DES galaxies into five photometric redshift bins, finding significant (>2σ) detections in all bins. Comparing to the fiducial Planck cosmology, we find the redshift evolution of the signal matches expectations, although the amplitude is consistently lower than predicted across redshift bins. We test for possible systematics that could affect our result and find no evidence for significant contamination. Finally, we demonstrate how these measurements can be used to constrain the growth of structure across cosmic time. We find the data are fit by a model in which the amplitude of structure in the z < 1.2 universe is 0.73 ± 0.16 times as large as predicted in the ΛCDM Planck cosmology, a 1.7σ deviation.

We study the clustering of galaxies detected at i < 22.5 in the Science Verification observations of the Dark Energy Survey (DES). Two-point correlation functions are measured using 2.3 × 10 6 galaxies over a contiguous 116 deg 2 region in five bins of photometric redshift width ∆z = 0.2 in the range 0.2 < z < 1.2. The impact of photometric redshift errors are assessed by comparing results using a template-based photo-z algorithm (BPZ) to a machine-learning algorithm (TPZ). A companion paper (Leistedt et al 2015) presents maps of several observational variables (e.g. seeing, sky brightness) which could modulate the galaxy density. Here we characterize and mitigate systematic errors on the measured clustering which arise from these observational variables, in addition to others such as Galactic dust and stellar contamination. After correcting for systematic effects we measure galaxy bias over a broad range of linear scales relative to mass clustering predicted from the Planck ΛCDM model, finding agreement with CFHTLS measurements with χ 2 of 4.0 (8.7) with 5 degrees of freedom for the TPZ (BPZ) redshifts. We test a "linear bias" model, in which the galaxy clustering is a fixed multiple of the predicted non-linear dark-matter clustering. The precision of the data allow us to determine that the linear bias model describes the observed galaxy clustering to 2.5% accuracy down to scales at least 4 to 10 times smaller than those on which linear theory is expected to be sufficient.

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 ± 0.01 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 δz 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 σ 8 of approx. 3%. This shift is within the one sigma statistical errors on σ 8 for the DES SV shear catalog. We further study the potential impact of systematic differences on the critical surface density, Σ 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.