Wide-field lensing mass maps from Dark Energy Survey science verification data: Methodology and detailed analysis

Vikram, V. and Chang, C. and Jain, B. and Bacon, D. and Amara, A. and Becker, M. R. and Bernstein, G. and Bonnett, C. and Bridle, S. and Brout, D. and Busha, M. and Frieman, J. and Gaztanaga, E. and Hartley, W. and Jarvis, M. and Kacprzak, T. and Kovács, András and Lahav, O. and Leistedt, B. and Lin, H. and Melchior, P. and Peiris, H. and Rozo, E. and Rykoff, E. and Sanchez, C. and Sheldon, E. and Troxel, M. A. and Wechsler, R. and Zuntz, J. and Abbott, T. (2015) Wide-field lensing mass maps from Dark Energy Survey science verification data: Methodology and detailed analysis. PHYSICAL REVIEW D, 92 (2). ISSN 2470-0010

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Abstract

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 cosmology as they probe both luminous and dark matter. In this paper, we present a weak lensing mass map reconstructed from shear measurements in a 139 deg2 area from the Dark Energy Survey (DES) Science Verification data. We compare the distribution of mass with that of the foreground distribution of galaxies and clusters. The overdensities in the reconstructed map correlate well with the distribution of optically detected clusters. We demonstrate that candidate superclusters and voids along the line of sight can be identified, exploiting the tight scatter of the cluster photometric redshifts. We cross-correlate the mass map with a foreground magnitude-limited galaxy sample from the same data. Our measurement gives results consistent with mock catalogs from N-body simulations that include the primary sources of statistical uncertainties in the galaxy, lensing, and photo-z catalogs. The statistical significance of the cross-correlation is at the 6.8σ level with 20 arcminute smoothing. We find that the contribution of systematics to the lensing mass maps is generally within measurement uncertainties. In this work, we analyze less than 3 % of the final area that will be mapped by the DES; the tools and analysis techniques developed in this paper can be applied to forthcoming larger datasets from the survey.

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