Data characterization using artificial-star tests: performance evaluation

Yi Hu*, Licai Deng, Richard de Grijs, Qiang Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)107-112
Number of pages6
JournalPublications of the Astronomical Society of the Pacific
Issue number899
Publication statusPublished - Jan 2011
Externally publishedYes


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