Online Program Home
  My Program Register!

Abstract Details

Activity Number: 83
Type: Invited
Date/Time: Sunday, July 30, 2017 : 8:30 PM to 10:30 PM
Sponsor: Astrostatistics Special Interest Group
Abstract #323188
Title: Testing Bayesian Galactic Mass Estimates Using Outputs from Hydrodynamical Simulations
Author(s): Gwendolyn Eadie* and Benjamin Keller and William Harris and Aaron Springford
Companies: McMaster University and McMaster University and McMaster University and Queen's University
Keywords: astrostatistics ; astronomy ; cosmology ; hierarchical ; Bayesian ; physics

In a series of three papers in the Astrophysical Journal, Eadie et al (2015), Eadie & Harris (2016) and Eadie et al (2017) developed a hierarchical Bayesian statistical framework for estimating the mass of the Milky Way Galaxy. The method confronts a physical model for the Galaxy with position and velocity data of objects such as globular clusters which orbit the Milky Way. The hierarchical Bayesian analysis of such data, which suffers from incompleteness and varying degrees of uncertainty, has led to a more constrained mass estimate for the Milky Way. However, the physical model used in this method is a simplification of the true structural complexity of the Milky Way (which includes a disk, bulge, and dark matter halo). To investigate the performance of the hierarchical Bayesian method with this underlying simple physical model, we apply the framework to mock observations of simulated Milky Way-type galaxies that were created using hydrodynamical and cosmological simulations. These simulated galaxies are state-of-the-art and represent our best current understanding of the Milky Way's structure and dynamical interactions.

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

Back to the full JSM 2017 program

Copyright © American Statistical Association