DNF - Galaxy photometric redshift by Directional Neighbourhood Fitting
  Wide field images taken in several photometric bands allow simultaneous
measurement of redshifts for thousands of galaxies. A variety of algorithms to
make this measurement have appeared in the last few years, the majority of
which can be classified as either template or training based methods. Among the
latter, nearest neighbour estimatorsstand out as one of the most successful, in
terms of both precision and the quality of error estimation. In this paper we
describe the Directional Neighbourhood Fitting (DNF) algorithm based on the
following: a new neighbourhood metric (Directional Neighbourhood), a photo-z
estimation strategy (Neighbourhood Fitting) and a method for generating the
photo-z probability distribution function. We compare DNF with other well-known
empirical photometric redshift tools using different public datasets (Sloan
Digital Sky Survey, VIMOS VLT Deep Survey and Photo-z Accuracy Testing). DNF
achieves high-quality results with reliable error.
