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Data Characterization Using Artificial-Star Tests: Performance Evaluation
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DOI：10.1086/658162.png)
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Abstract
En 中文
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 article, 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 to 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.
Keywords:
LARGE-MAGELLANIC-CLOUD
TELESCOPE WFPC2 PHOTOMETRY
CLUSTER NGC 1818
GLOBULAR-CLUSTERS
LUMINOSITY FUNCTIONS
MASS SEGREGATION
MAIN-SEQUENCE
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