Probing the sparse tails of redshift distributions with Voronoi
  tessellations
  We introduce an algorithm to estimate the redshift distribution of a sample
of galaxies selected photometrically given a subsample with measured
spectroscopic redshifts. The approach uses a non-parametric Voronoi
tessellation density estimator to interpolate the galaxy distribution in the
redshift and photometric color space. We test the method on a mock dataset with
a known color-redshift distribution. We find that the Voronoi tessellation
estimator performs well at reconstructing the tails of the redshift
distribution of individual galaxies and gives unbiased estimates of the first
and second moments. The source code is publicly available at
http://bitbucket.org/bengranett/tailz.
