Recovering Redshift Distributions with Cross-Correlations: Pushing The
  Boundaries
  Determining accurate redshift distributions for very large samples of objects
has become increasingly important in cosmology. We investigate the impact of
extending cross-correlation based redshift distribution recovery methods to
include small scale clustering information. The major concern in such work is
the ability to disentangle the amplitude of the underlying redshift
distribution from the influence of evolving galaxy bias. Using multiple
simulations covering a variety of galaxy bias evolution scenarios, we
demonstrate reliable redshift recoveries using linear clustering assumptions
well into the non-linear regime for redshift distributions of narrow redshift
width. Including information from intermediate physical scales balances the
increased information available from clustering and the residual bias incurred
from relaxing of linear constraints. We discuss how breaking a broad sample
into tomographic bins can improve estimates of the redshift distribution, and
present a simple bias removal technique using clustering information from the
spectroscopic sample alone.
