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.