Statistical Challenges in the Sky: Distribution Functions, Data, and Inference for Stellar Systems

Astrostatistics lies at the interface of astrophysics and statistics and has been growing for over two decades. Reasons for this growth include big datasets in astronomy that are often publicly available, the use of physically motivated mathematical models in inference, and the need for innovative and efficient statistical and computational methods. To highlight the kinds of statistical challenges and opportunities available in astrostatistics, we focus in this review on a specific type of astronomical objects called globular clusters. These star clusters are some of the oldest objects in the universe and are deceptively simple in appearance. In the context of studying them, we introduce and highlight challenges and opportunities related to modeling, data, inference, and computation that should be of interest to statisticians. We hope this review will inspire statisticians to become involved not only in the study of globular clusters but also in astrostatistics.