Robust, data-driven inference in non-linear cosmostatistics
  We discuss two projects in non-linear cosmostatistics applicable to very
large surveys of galaxies. The first is a Bayesian reconstruction of galaxy
redshifts and their number density distribution from approximate, photometric
redshift data. The second focuses on cosmic voids and uses them to construct
cosmic spheres that allow reconstructing the expansion history of the Universe
using the Alcock-Paczynski test. In both cases we find that non-linearities
enable the methods or enhance the results: non-linear gravitational evolution
creates voids and our photo-z reconstruction works best in the highest density
(and hence most non-linear) portions of our simulations.
