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McMaster University

1. Eadie, Gwendolyn. Lights in Dark Places: Inferring the Milky Way Mass Profile using Galactic Satellites and Hierarchical Bayes.

Degree: PhD, 2017, McMaster University

Despite valiant effort by astronomers, the mass of the Milky Way (MW) Galaxy is poorly constrained, with estimates varying by a factor of two. A range of techniques have been developed and different types of data have been used to estimate the MW’s mass. One of the most promising and popular techniques is to use the velocity and position information of satellite objects orbiting the Galaxy to infer the gravitational potential, and thus the total mass. Using these satellites, or Galactic tracers, presents a number of challenges: 1) much of the tracer velocity data are incomplete (i.e. only line-of-sight velocities have been measured), 2) our position in the Galaxy complicates how we quantify measurement uncertainties of mass estimates, and 3) the amount of available tracer data at large distances, where the dark matter halo dominates, is small. The latter challenge will improve with current and upcoming observational programs such as Gaia and the Large Synoptic Survey Telescope (LSST), but to properly prepare for these data sets we must overcome the former two. In this thesis work, we have created a hierarchical Bayesian framework to estimate the Galactic mass profile. The method includes incomplete and complete data simultaneously, and incorporates measurement uncertainties through a measurement model. The physical model relies on a distribution function for the tracers that allows the tracer and dark matter to have different spatial density profiles. When the hierarchical Bayesian model is confronted with the kinematic data from satellites, a posterior distribution is acquired and used to infer the mass and mass profile of the Galaxy. This thesis walks through the incremental steps that led to the development of the hierarchical Bayesian method, and presents MW mass estimates when the method is applied to the MW’s globular cluster population. Our best estimate of the MW’s virial mass is 0.87 (0.67, 1.09) x 10^(12) solar masses. We also present preliminary results from a blind test on hydrodynamical, cosmological computer-simulated MW-type galaxies from the McMaster Unbiased Galaxy Simulations. These results suggest our method may be able to reliably recover the virial mass of the Galaxy.

Thesis

Doctor of Philosophy (PhD)

Advisors/Committee Members: Harris, William, Physics and Astronomy.

Subjects/Keywords: Milky Way; Bayesian; Galaxy; Astronomy; Astrostatistics; dark matter

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Eadie, G. (2017). Lights in Dark Places: Inferring the Milky Way Mass Profile using Galactic Satellites and Hierarchical Bayes. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/22008

Chicago Manual of Style (16th Edition):

Eadie, Gwendolyn. “Lights in Dark Places: Inferring the Milky Way Mass Profile using Galactic Satellites and Hierarchical Bayes.” 2017. Doctoral Dissertation, McMaster University. Accessed October 18, 2017. http://hdl.handle.net/11375/22008.

MLA Handbook (7th Edition):

Eadie, Gwendolyn. “Lights in Dark Places: Inferring the Milky Way Mass Profile using Galactic Satellites and Hierarchical Bayes.” 2017. Web. 18 Oct 2017.

Vancouver:

Eadie G. Lights in Dark Places: Inferring the Milky Way Mass Profile using Galactic Satellites and Hierarchical Bayes. [Internet] [Doctoral dissertation]. McMaster University; 2017. [cited 2017 Oct 18]. Available from: http://hdl.handle.net/11375/22008.

Council of Science Editors:

Eadie G. Lights in Dark Places: Inferring the Milky Way Mass Profile using Galactic Satellites and Hierarchical Bayes. [Doctoral Dissertation]. McMaster University; 2017. Available from: http://hdl.handle.net/11375/22008

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