DETECTING BIMODALITY IN ASTRONOMICAL DATASETS

We discuss statistical techniques for detecting and quantifying bimoda lity in astronomical datasets. We concentrate on the KMM algorithm, wh ich estimates the statistical significance of bimodality in such datas ets and objectively partitions data into subpopulations. By simulating bimodal distributions with a range of properties we investigate the s ensitivity of KMM to datasets with varying characteristics. Our result s facilitate the planning of optimal observing strategies for systems where bimodality is suspected. Mixture-modeling algorithms similar to the KMM algorithm have been used in previous studies to partition the stellar population of the Milky Way into subsystems. We illustrate the broad applicability of KMM by analyzing published data on globular cl uster metallicity distributions, velocity distributions of galaxies in clusters, and burst durations of gamma-ray sources. FORTRAN code for the KMM algorithm and directions for its use are available from the au thors upon request.