0 Assigned ID: 1020 ----- Attempting Submission 1 (Mon Feb 18 21:56:31 GMT 2013) ----- HST has allowed us to observe both nearby and distant galaxies with unprecedented resolution and sensitivity, enabling us to probe to ever-smaller spatial scales, masses, and star formation rates. However, many of the stellar population synthesis (SPS) models we use to extract physical quantities from these observations do not properly account for the stochastic effects that become important when we cannot assume that an observed stellar population adequately samples all possible stellar masses and evolutionary stages. In this regime, stochastic sampling introduces fundamental and, at present, largely uncalibrated limitations on the information that can be gleaned from unresolved photometry. Last year we introduced a new code, Stochastically Lighitng Up Galaxies (SLUG), for SPS in the stochastic regime. We now propose to expand SLUG's capabilties to make it an even more powerful tool for analyzing HST data. We will use SLUG models to characterize the amount of stochastic scatter in various filters expected as a result of stochasticity, and the corresponding uncertainty this induces in estimates of various parameters (e.g. star formation rates, cluster masses, dust extinctions). We will provide software tools to calculate probability distributions for these parameters from observations in a variety of filters. We will also couple SLUG to the photoionization code CLOUDY, enabling for the first time a proper stochastic treatment of nebular line emission as well. The resulting code will be released to the community, facilitating greater understanding of the uncertainties in past, present, and future HST observations, and assisting observers in planning future campaigns. UNRESOLVED STAR FORMATION Default Medium false false false false true SUPPORTED