  The False Discovery Rate (FDR) method has recently been described by Miller
et al (2001), along with several examples of astrophysical applications. FDR is
a new statistical procedure due to Benjamini and Hochberg (1995) for
controlling the fraction of false positives when performing multiple hypothesis
testing. The importance of this method to source detection algorithms is
immediately clear. To explore the possibilities offered we have developed a new
task for performing source detection in radio-telescope images, Sfind 2.0,
which implements FDR. We compare Sfind 2.0 with two other source detection and
measurement tasks, Imsad and SExtractor, and comment on several issues arising
from the nature of the correlation between nearby pixels and the necessary
assumption of the null hypothesis. The strong suggestion is made that
implementing FDR as a threshold defining method in other existing
source-detection tasks is easy and worthwhile. We show that the constraint on
the fraction of false detections as specified by FDR holds true even for highly
correlated and realistic images. For the detection of true sources, which are
complex combinations of source-pixels, this constraint appears to be somewhat
less strict. It is still reliable enough, however, for a priori estimates of
the fraction of false source detections to be robust and realistic.
