  Traditional artificial-star tests are widely applied to photometry in crowded
stellar fields. However, to obtain reliable binary fractions (and their
uncertainties) of remote, dense, and rich star clusters, one needs to recover
huge numbers of artificial stars. Hence, this will consume much computation
time for data reduction of the images to which the artificial stars must be
added. In this paper, we present a new method applicable to data sets
characterized by stable, well-defined point-spread functions, in which we add
artificial stars to the retrieved-data catalog instead of the raw images.
Taking the young Large Magellanic Cloud cluster NGC 1818 as an example, we
compare results from both methods and show that they are equivalent, while our
new method saves significant computational time.
