335 – Complex and High-Dimensional Inference in Astrostatistics
Incomplete Data and Measurement Error in the Galactic Mass Estimation Problem
Gwendolyn Eadie
McMaster University
William Harris
McMaster University
Aaron Springford
Queen's University
It is widely accepted that the Milky Way Galaxy resides within a massive dark matter halo. The mass and cumulative mass profile of this halo (and the Galaxy as a whole) is one of the most fundamental properties of the Galaxy. Estimating these properties, however, is not a trivial problem. We rely on the kinematic information of satellites which orbit the Galaxy, such as globular clusters and dwarf galaxies, and this data is incomplete and subject to measurement uncertainty. In particular, the complete 3D velocity and position vectors of objects are sometimes unavailable, and there are selection biases due to the distribution of objects around the Galaxy and our measurement position. On the other hand, the instrumental uncertainties of telescopes that collect this data is fairly well understood. Thus, we would like to incorporate these uncertainties into our estimate of the Milky Way's mass. The Bayesian paradigm offers a way to deal with both the missing kinematic data and measurement errors using a hierarchical model.