SLUG IV: A Novel Forward-Modelling Method to Derive the Demographics of
  Star Clusters
  We describe a novel method for determining the demographics of a population
of star clusters, for example distributions of cluster mass and age, from
unresolved photometry. This method has a number of desirable properties: it
fully exploits all the information available in a data set without any binning,
correctly accounts for both measurement error and sample incompleteness,
naturally handles heterogenous data (for example fields that have been imaged
with different sets of filters or to different depths), marginalises over
uncertain extinctions, and returns the full posterior distributions of the
parameters describing star cluster demographics. We demonstrate the method
using mock star cluster catalogs and show that our method is robust and
accurate, and that it can recover the demographics of star cluster populations
significantly better than traditional fitting methods. For realistic sample
sizes, our method is sufficiently powerful that its accuracy is ultimately
limited by the accuracy of the underlying physical models for stellar evolution
and interstellar dust, rather than by statistical uncertainties. Our method is
implemented as part of the Stochastically Lighting Up Galaxies (SLUG) stellar
populations code, and is freely available.
