1k citations · 4.5k across the 35 of their papers we have counts for
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Cross-correlation of gravitational lensing from DES Science Verification data with SPT and Planck lensing
D. Kirk, Y. Omori, A. Benoit-Lévy +89
We measure the cross-correlation between weak lensing of galaxy images and of the cosmic microwave background (CMB). The effects of gravitational lensing on different sources will…
Redshift distributions of galaxies in the DES Science Verification shear catalogue and implications for weak lensing
C. Bonnett, M. A. Troxel, W. Hartley +104
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 learni…
Mapping and simulating systematics due to spatially-varying observing conditions in DES Science Verification data
B. Leistedt, H. V. Peiris, F. Elsner +92
Spatially-varying depth and characteristics of observing conditions, such as seeing, airmass, or sky background, are major sources of systematic uncertainties in modern galaxy surv…
redMaGiC: Selecting Luminous Red Galaxies from the DES Science Verification Data
E. Rozo, E. S. Rykoff, A. Abate +84
We introduce redMaGiC, an automated algorithm for selecting Luminous Red Galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in…
The DES Science Verification Weak Lensing Shear Catalogues
M. Jarvis, E. Sheldon, J. Zuntz +92
We present weak lensing shear catalogues for 139 square degrees of data taken during the Science Verification (SV) time for the new Dark Energy Camera (DECam) being used for the Da…
Cosmic Shear Measurements with DES Science Verification Data
M. R. Becker, M. A. Troxel, N. MacCrann +102
We present measurements of weak gravitational lensing cosmic shear two-point statistics using Dark Energy Survey Science Verification data. We demonstrate that our results are robu…
Cosmology from Cosmic Shear with DES Science Verification Data
The Dark Energy Survey Collaboration, T. Abbott, F. B. Abdalla +126
We present the first constraints on cosmology from the Dark Energy Survey (DES), using weak lensing measurements from the preliminary Science Verification (SV) data. We use 139 squ…
Galaxy clustering, photometric redshifts and diagnosis of systematics in the DES Science Verification data
M. Crocce, J. Carretero, A. H. Bauer +84
We study the clustering of galaxies detected at in the Science Verification observations of the Dark Energy Survey (DES). Two-point correlation functions are measured usin…
Weak lensing by galaxy troughs in DES Science Verification data
D. Gruen, O. Friedrich, A. Amara +89
We measure the weak lensing shear around galaxy troughs, i.e. the radial alignment of background galaxies relative to underdensities in projections of the foreground galaxy field o…
Wide-Field Lensing Mass Maps from DES Science Verification Data
C. Chang, V. Vikram, B. Jain +81
We present a mass map reconstructed from weak gravitational lensing shear measurements over 139 sq. deg from the Dark Energy Survey (DES) Science Verification data. The mass map pr…
Wide-Field Lensing Mass Maps from DES Science Verification Data: Methodology and Detailed Analysis
V. Vikram, C. Chang, B. Jain +91
Weak gravitational lensing allows one to reconstruct the spatial distribution of the projected mass density across the sky. These "mass maps" provide a powerful tool for studying c…
Anomaly detection for machine learning redshifts applied to SDSS galaxies
Ben Hoyle, Markus Michael Rau, Kerstin Paech +3
We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence…
The galaxy-halo connection from a joint lensing, clustering and abundance analysis in the CFHTLenS/VIPERS field
J. Coupon, S. Arnouts, L. van Waerbeke +31
We present new constraints on the relationship between galaxies and their host dark matter halos, measured from the location of the peak of the stellar-to-halo mass ratio (SHMR), u…
Data augmentation for machine learning redshifts applied to SDSS galaxies
Ben Hoyle, Markus Michael Rau, Christopher Bonnett +2
We present analyses of data augmentation for machine learning redshift estimation. Data augmentation makes a training sample more closely resemble a test sample, if the two base sa…