Cross-Identification Performance from Simulated Detections: GALEX and
  SDSS
  We investigate the quality of associations of astronomical sources from
multi-wavelength observations using simulated detections that are realistic in
terms of their astrometric accuracy, small-scale clustering properties and
selection functions. We present a general method to build such mock catalogs
for studying associations, and compare the statistics of cross-identifications
based on angular separation and Bayesian probability criteria. In particular,
we focus on the highly relevant problem of cross-correlating the ultraviolet
Galaxy Evolution Explorer (GALEX) and optical Sloan Digital Sky Survey (SDSS)
surveys. Using refined simulations of the relevant catalogs, we find that the
probability thresholds yield lower contamination of false associations, and are
more efficient than angular separation. Our study presents a set of recommended
criteria to construct reliable cross-match catalogs between SDSS and GALEX with
minimal artifacts.
