1k citations · 3.5k across the 107 of their papers we have counts for
6 papers · 1 filter
The Dark Energy Survey: more than dark energy - an overview
Dark Energy Survey Collaboration, T. Abbott, F. B. Abdalla +138
This overview article describes the legacy prospect and discovery potential of the Dark Energy Survey (DES) beyond cosmological studies, illustrating it with examples from the DES…
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…
ANNz2 - photometric redshift and probability distribution function estimation using machine learning
Iftach Sadeh, Filipe B. Abdalla, Ofer Lahav
We present ANNz2, a new implementation of the public software for photometric redshift (photo-z) estimation of Collister and Lahav (2004), which now includes generation of full pro…
Gravitational Redshift of Galaxies in Clusters from the Sloan Digital Sky Survey and the Baryon Oscillation Spectroscopic Survey
Iftach Sadeh, Low Lerh Feng, Ofer Lahav
The gravitational redshift effect allows one to directly probe the gravitational potential in clusters of galaxies. Following up on Wojtak et al. [Nature (London) 477, 567 (2011)],…
Spectroscopic Needs for Imaging Dark Energy Experiments: Photometric Redshift Training and Calibration
J. Newman, A. Abate, F. Abdalla +58
Large sets of objects with spectroscopic redshift measurements will be needed for imaging dark energy experiments to achieve their full potential, serving two goals:_training_, i.e…