C. E. Cunha

A Galaxy Photometric Redshift Catalog for the Sloan Digital Sky Survey Data Release 6

We present and describe a catalog of galaxy photometric redshifts (photo-zs) for the Sloan Digital Sky Survey (SDSS) Data Release 6 (DR6). We use the neural network (NN) technique to calculate photo-zs and the nearest neighbor error (NNE) method to estimate photo-z errors for ~77 million objects classified as galaxies in DR6 with r < 22. The photo-z and photo-z error estimators are trained and validated on a sample of ~640,000 galaxies that have SDSS photometry and spectroscopic redshifts measured by SDSS, the Two Degree Field, the SDSS Luminous Red Galaxy and Quasi-stellar Object Survey (2SLAQ), the Canada-France Redshift Survey (CFRS), the Canadian Network for Observational Cosmology Field Galaxy Survey (CNOC2), the Team Keck Redshift Survey (TKRS), the Deep Extragalactic Evolutionary Probe (DEEP), and DEEP2. For the two best NN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than σ68 = 0.021 or 0.024. After presenting our results and quality tests, we provide a short guide for users accessing the public data.

Cross-correlation Weak Lensing of SDSS Galaxy Clusters. I. Measurements

This is the first in a series of papers on the weak lensing effect caused by clusters of galaxies in Sloan Digital Sky Survey. The photometrically selected cluster sample, known as MaxBCG, includes ~130,000 objects between redshift 0.1 and 0.3, ranging in size from small groups to massive clusters. We split the clusters into bins of richness and luminosity and stack the surface density contrast to produce mean radial profiles. The mean profiles are detected over a range of scales, from the inner halo (25 kpc h^(–1)) well into the surrounding large-scale structure (30 Mpc h^(–1)), with a significance of 15 to 20 in each bin. The signal over this large range of scales is best interpreted in terms of the cluster-mass cross-correlation function. We pay careful attention to sources of systematic error, correcting for them where possible. The resulting signals are calibrated to the ~10% level, with the dominant remaining uncertainty being the redshift distribution of the background sources. We find that the profiles scale strongly with richness and luminosity. We find that the signal within a given richness bin depends upon luminosity, suggesting that luminosity is more closely correlated with mass than galaxy counts. We split the samples by redshift but detect no significant evolution. The profiles are not well described by power laws. In a subsequent series of papers, we invert the profiles to three-dimensional mass profiles, show that they are well fit by a halo model description, measure mass-to-light ratios, and provide a cosmological interpretation.

Photometric redshift analysis in the Dark Energy Survey science verification data

We present results from a study of the photometric redshift performance of the Dark Energy Survey (DES), using the early data from a Science Verification (SV) period of observations in late 2012 and early 2013 that provided science-quality images for almost 200 sq.~deg.~at the nominal depth of the survey. We assess the photometric redshift performance using about 15000 galaxies with spectroscopic redshifts available from other surveys. These galaxies are used, in different configurations, as a calibration sample, and photo-

Precision photometric redshift calibration for galaxy-galaxy weak lensing

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Estimating the redshift distribution of photometric galaxy samples – II. Applications and tests of a new method

s are obtained and studied using most of the existing photo-

Estimating the redshift distribution of photometric galaxy samples

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The DES Science Verification weak lensing shear catalogues

codes. A weighting method in a multi-dimensional color-magnitude space is applied to the spectroscopic sample in order to evaluate the photo-

Stellar Kinematics and Metallicities in the Ultra-Faint Dwarf Galaxy Reticulum II

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The RedMaPPer Galaxy Cluster Catalog From DES Science Verification Data

performance with sets that mimic the full DES photometric sample, which is on average significantly deeper than the calibration sample due to the limited depth of spectroscopic surveys. Empirical photo-

Primordial non-Gaussianity from the covariance of galaxy cluster counts

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