Cosmic Voids and Void Lensing in the Dark Energy Survey Science Verification Data

Author
Sánchez, C · Clampitt, J · Kovacs, A · Jain, B · García-Bellido, J · Nadathur, S · Gruen, D · Hamaus, N · Huterer, D · Vielzeuf, P · Amara, A · Bonnett, C · DeRose, J · Hartley, W. G · Jarvis, M · Lahav, O · Miquel, R · Rozo, E · Rykoff, E. S · Sheldon, E · Wechsler, R. H · Zuntz, J · Abbott, T. M. C · Abdalla, F. B · Annis, J · Benoit-Lévy, A · Bernstein, G. M · Bernstein, R. A · Bertin, E · Brooks, D et al.
Year 2016
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Abstract

Galaxies and their dark matter halos populate a complicated filamentary network around large, nearly empty regions known as cosmic voids. Cosmic voids are usually identified in spectroscopic galaxy surveys, where 3D information about the large-scale structure of the Universe is available. Although an increasing amount of photometric data is being produced, its potential for void studies is limited since photometric redshifts induce line-of-sight position errors of $\sim50$ Mpc/$h$ or more that can render many voids undetectable. In this paper we present a new void finder designed for photometric surveys, validate it using simulations, and apply it to the high-quality photo-$z$ redMaGiC galaxy sample of the Dark Energy Survey Science Verification (DES-SV) data. The algorithm works by projecting galaxies into 2D slices and finding voids in the smoothed 2D galaxy density field of the slice. Fixing the line-of-sight size of the slices to be at least twice the photo-$z$ scatter, the number of voids found in these projected slices of simulated spectroscopic and photometric galaxy catalogs is within 20% for all transverse void sizes, and indistinguishable for the largest voids of radius $\sim 70$ Mpc/$h$ and larger. The positions, radii, and projected galaxy profiles of photometric voids also accurately match the spectroscopic void sample. Applying the algorithm to the DES-SV data in the redshift range $0.2

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Title
Cosmic Voids and Void Lensing in the Dark Energy Survey Science Verification Data
Author
Sánchez, C · Clampitt, J · Kovacs, A · Jain, B · García-Bellido, J · Nadathur, S · Gruen, D · Hamaus, N · Huterer, D · Vielzeuf, P · Amara, A · Bonnett, C · DeRose, J · Hartley, W. G · Jarvis, M · Lahav, O · Miquel, R · Rozo, E · Rykoff, E. S · Sheldon, E · Wechsler, R. H · Zuntz, J · Abbott, T. M. C · Abdalla, F. B · Annis, J · Benoit-Lévy, A · Bernstein, G. M · Bernstein, R. A · Bertin, E · Brooks, D · Buckley-Geer, E · Rosell, A. Carnero · Kind, M. Carrasco · Carretero, J · Crocce, M · Cunha, C. E · D'Andrea, C. B · da Costa, L. N · Desai, S · Diehl, H. T · Dietrich, J. P · Doel, P · Evrard, A. E · Neto, A. Fausti · Flaugher, B · Fosalba, P · Frieman, J · Gaztanaga, E · Gruendl, R. A · Gutierrez, G · Honscheid, K · James, D. J · Krause, E · Kuehn, K · Lima, M · Maia, M. A. G · Marshall, J. L · Melchior, P · Plazas, A. A · Reil, K · Romer, A. K · Sanchez, E · Schubnell, M · Sevilla-Noarbe, I · Smith, R. C · Soares-Santos, M · Sobreira, F · Suchyta, E · Tarle, G · Thomas, D · Walker, A. R · Weller, J
Year
2016
Type
Research Article
Language
eng
Comment
Accepted for publication in MNRAS. 15 pages, 16 figures
History
2016-05-12 00:00:00 · 2016-05-30 00:00:00 · 2016-10-21 00:00:00

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