Bayesian Inference for Old Star Clusters: studying coverage probabilities through repeated simulation

• Activity Number: 363 - Astrostatistics: Telling the Story of Our Galaxy and Beyond
• Type: Invited
• Date/Time: Thursday, August 12, 2021 : 12:00 PM to 1:50 PM
• Sponsor: SSC (Statistical Society of Canada)
• Author(s): Gwendolyn Eadie* and Jeremy Webb and Jeffrey Rosenthal
• Companies: University of Toronto and University of Toronto and University of Toronto
• Keywords: Bayesian inference; physical sciences; missing data; measurement uncertainty; astronomy; astrostatistics

Abstract

In 2018, the Gaia Collaboration of the European Space Agency released a massive public data set ("Gaia DR2") of over 1 billion stars in the Milky Way Galaxy, including stars that reside in old star clusters. Inferring the distribution of stellar mass and velocity in these clusters is vital to testing physical theories about star cluster evolution. While Gaia DR2 is a "stellar" data set, it also presents statistical challenges such as measurement uncertainty, selection bias, incompleteness, and truncation. Through repeated simulations of star clusters and using a Bayesian analysis framework, we show parameter inference and coverage probabilities are reliable in the case of complete data. However, we also find that selection bias leads to unreliable credible intervals that would need to be accounted for in a real analysis. Ultimately, our aim is to develop a computationally efficient and reliable data analysis pipeline that can handle many clusters simultaneously, where each cluster has on the order of 10,000-100,000 stars. In this talk, I will go over our most recent advancements (and challenges) in this project, including our tests with simulated data.