Dark matter voids in the SDSS galaxy survey

Abstract

What do we know about voids in the dark matter distribution given the Sloan Digital Sky Survey (SDSS) and assuming the $\Lambda \mathrm{CDM}$ model? Recent application of the Bayesian inference algorithm BORG to the SDSS Data Release 7 main galaxy sample has generated detailed Eulerian and Lagrangian representations of the large-scale structure as well as the possibility to accurately quantify corresponding uncertainties. Building upon these results, we present constrained catalogs of voids in the Sloan volume, aiming at a physical representation of dark matter underdensities and at the alleviation of the problems due to sparsity and biasing on galaxy void catalogs. To do so, we generate data-constrained reconstructions of the presently observed large-scale structure using a fully non-linear gravitational model. We then find and analyze void candidates using the VIDE toolkit. Our methodology therefore predicts the properties of voids based on fusing prior information from simulations and data constraints. For usual void statistics (number function, ellipticity distribution and radial density profile), all the results obtained are in agreement with dark matter simulations. Our dark matter void candidates probe a deeper void hierarchy than voids directly based on the observed galaxies alone. The use of our catalogs therefore opens the way to high-precision void cosmology at the level of the dark matter field. We will make the void catalogs used in this work available at http://www.cosmicvoids.net.

15 pages, 6 figures, matches JCAP published version, void catalogs publicly available at http://www.cosmicvoids.net

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Cited by in corpus (8)

• Bayesian analysis of the dynamic cosmic web in the SDSS galaxy survey
• Voids and the Cosmic Web: cosmic depressions & spatial complexity
• Cosmic voids detection without density measurements
• sCOLA: The N-body COLA Method Extended to the Spatial Domain
• Cosmic web-type classification using decision theory
• On the Abundance of Extreme Voids
• Bayesian Cosmic Web Reconstruction: BARCODE for Clusters
• Bayesian inference of dark matter voids in galaxy surveys