2dFLenS and KiDS: Determining source redshift distributions with
  cross-correlations
  We develop a statistical estimator to infer the redshift probability
distribution of a photometric sample of galaxies from its angular
cross-correlation in redshift bins with an overlapping spectroscopic sample.
This estimator is a minimum variance weighted quadratic function of the data: a
quadratic estimator. This extends and modifies the methodology presented by
McQuinn & White (2013). The derived source redshift distribution is degenerate
with the source galaxy bias, which must be constrained via additional
assumptions. We apply this estimator to constrain source galaxy redshift
distributions in the Kilo-Degree imaging survey through cross-correlation with
the spectroscopic 2-degree Field Lensing Survey, presenting results first as a
binned step-wise distribution in the range z < 0.8, and then building a
continuous distribution using a Gaussian process model. We demonstrate the
robustness of our methodology using mock catalogues constructed from N-body
simulations, and comparisons with other techniques for inferring the redshift
distribution.
