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 &
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
worhtwhile. 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. Further investigation of the relationship
between `source-pixels' and `sources' is nevertheless important to
more strictly constrain the fraction of falsely detected sources.