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Activity Number: 220261 - 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)
Abstract #316593
Title: Hierarchical Bayesian Analysis for Old Star Clusters
Author(s): Gwendolyn Eadie*
Companies: University of Toronto
Keywords: Bayesian inference; physical sciences; missing data; measurement uncertainty; astronomy; astrostatistics

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 --- Globular Clusters. Inferring the distribution of stellar mass in these globular clusters is vital to testing physical theories about cluster evolution. While Gaia DR2 is a stellar data set (pun intended), it also presents statistical challenges such as measurement uncertainty, incompleteness, and truncation. By pooling information across multiple globular clusters in the Milky Way Galaxy through a hierarchical Bayesian model, we hope to better constrain the masses of these systems. At the same time, we need to develop a computationally efficient and reliable data analysis pipeline to work with all clusters simultaneously in the hierarchical Bayesian framework, because each globular cluster has on the order of 10,000-100,000 stars. In this talk, I will go over our most recent advancements in this project, including tests with simulated data.

Authors who are presenting talks have a * after their name.

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