Probabilistic Cross-Identification of Galaxies with Realistic Clustering
  Probabilistic cross-identification has been successfully applied to a number
of problems in astronomy from matching simple point sources to associating
stars with unknown proper motions and even radio observations with realistic
morphology. Here we study the Bayes factor for clustered objects and focus in
particular on galaxies to assess the effect of typical angular correlations.
Numerical calculations provide the modified relationship, which (as expected)
suppresses the evidence for the associations at the shortest separations where
the 2-point auto-correlation function is large. Ultimately this means that the
matching probability drops at somewhat shorter scales than in previous models.
