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Abstract:
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Globular Clusters (GCs) are astronomical objects made up of tens of thousands to hundreds of thousands of stars. GCs are some of the oldest objects in the universe and are incredibly spatially dense, making them interesting laboratories for studying stellar populations. In particular, estimates of a GC’s mass as a function of radius can be used to test theories about GC evolution. However, the high spatial density of GCs is both a blessing and a curse --- there is a large population of stars to observe in the outer regions of a GC, but it is impossible to discern individual stars in the inner regions because of extreme crowding. Thus, astronomers usually estimate a GC’s mass as a function of radius by first estimating the total light in radial bins, and then assuming a mass-to-light ratio. I will present a Bayesian approach that negates both the need for binning data and the assumption about the mass-to-light ratio, and that instead takes advantage of position and velocity information from a sample of individual stars. I will also discuss the statistical and computational challenges we face while including measurement uncertainties, projection effects, and incomplete data.
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