Dark matter voids in the SDSS galaxy survey

Understand

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.