Bayesian inference of dark matter voids in galaxy surveys
  We apply the BORG algorithm to the Sloan Digital Sky Survey Data Release 7
main sample galaxies. The method results in the physical inference of the
initial density field at a scale factor $a~=~10^{-3}$, evolving gravitationally
to the observed density field at a scale factor $a~=~1$, and provides an
accurate quantification of corresponding uncertainties. Building upon these
results, we generate a set of constrained realizations of the present
large-scale dark matter distribution. As a physical illustration, we apply a
void identification algorithm to them. In this fashion, we access voids defined
by the inferred dark matter field, not by galaxies, greatly alleviating the
issues due to the sparsity and bias of tracers. In addition, the use of
full-scale physical density fields yields a drastic reduction of statistical
uncertainty in void catalogs. These new catalogs are enhanced data sets for
cross-correlation with other cosmological probes.
