Statistically speaking, “false sources” are generally in the domain of Type II Type I errors, defined by the probability of detecting a signal where there is none. But what if there is a clear signal, but it is not real?

In astronomical analysis, sources are generally defined with reference to the existing background, as point-fluctuations that exceed some significance threshold defined by the estimated background “in the vicinity”. The threshold is usually set such that we can tolerate “a few” false positives at borderline significance. But that ignores the effect of systematic deviations that can be caused by various instrumental features. Such things are common in X-ray images — window support structures, chip gaps, bad CCD columns, cosmic-ray hits, etc. Optical data are generally cleaner, but by no means immune to the problem. Barron et al. here describe how they have gone through the USNO-B catalog and have modeled and eliminated artifacts coming from diffraction spikes and telescope reflection halos of bright stars.

The bad news? More than 2.3% of the sources are flagged as spurious. Compare to the typical statistical significance at which the detection thresholds are set (usually >3sigma).