SLUG -- Stochastically Lighting Up Galaxies. III: A Suite of Tools for
  Simulated Photometry, Spectroscopy, and Bayesian Inference with Stochastic
  Stellar Populations
  Stellar population synthesis techniques for predicting the observable light
emitted by a stellar population have extensive applications in numerous areas
of astronomy. However, accurate predictions for small populations of young
stars, such as those found in individual star clusters, star-forming dwarf
galaxies, and small segments of spiral galaxies, require that the population be
treated stochastically. Conversely, accurate deductions of the properties of
such objects also requires consideration of stochasticity. Here we describe a
comprehensive suite of modular, open-source software tools for tackling these
related problems. These include: a greatly-enhanced version of the slug code
introduced by da Silva et al. (2012), which computes spectra and photometry for
stochastically- or deterministically-sampled stellar populations with
nearly-arbitrary star formation histories, clustering properties, and initial
mass functions; cloudy_slug, a tool that automatically couples slug-computed
spectra with the cloudy radiative transfer code in order to predict stochastic
nebular emission; bayesphot, a general-purpose tool for performing Bayesian
inference on the physical properties of stellar systems based on unresolved
photometry; and cluster_slug and sfr_slug, a pair of tools that use bayesphot
on a library of slug models to compute the mass, age, and extinction of
mono-age star clusters, and the star formation rate of galaxies, respectively.
The latter two tools make use of an extensive library of pre-computed stellar
population models, which are included the software. The complete package is
available at http://www.slugsps.com.
