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Tracing the Galactic Halo: Obtaining Bayesian mass estimates of the Galaxy in the presence of incomplete data

Published online by Cambridge University Press:  09 May 2016

Gwendolyn Eadie ,
William Harris ,
Lawrence Widrow  and
Aaron Springford
Gwendolyn Eadie
Affiliation:
Dept. of Physics & Astronomy, McMaster University, 1280 Main St. W, Hamilton, ON, L8S 4M1, Canada email: [email protected], [email protected]
William Harris
Affiliation:
Dept. of Physics & Astronomy, McMaster University, 1280 Main St. W, Hamilton, ON, L8S 4M1, Canada email: [email protected], [email protected]
Lawrence Widrow
Affiliation:
Dept. of Physics, Engineering Physics, & Astronomy, Queen's University Stirling Hall, 64 Bader Lane, Kingston, ON, K7L 3N6 email: [email protected]
Aaron Springford
Affiliation:
Dept. of Mathematics & Statistics, Queen's University Jeffrey Hall, University Drive, Kingston, ON, K7L 3N6, Canada email: [email protected]
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Abstract

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The mass and cumulative mass profile of the Galaxy are its most fundamental properties. Estimating these properties, however, is not a trivial problem. We rely on the kinematic information from Galactic satellites such as globular clusters and dwarf galaxies, and this data is incomplete and subject to measurement uncertainty. In particular, the complete 3D velocity vectors of objects are sometimes unavailable, and there may be selection biases due to both the distribution of objects around the Galaxy and our measurement position. On the other hand, the uncertainties of these data are fairly well understood. Thus, we would like to incorporate these uncertainties and the incomplete data 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. An application of this method to the Milky Way halo mass profile, using the kinematic data for globular clusters and dwarf satellites, is shown.

Keywords

dark matterGalaxy: generalhalokinematics and dynamicsmethods: statistical
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 11 , Issue S317: The General Assembly of Galaxy Halos: Structure, Origin and Evolution , August 2015 , pp. 296 - 297
Copyright
Copyright © International Astronomical Union 2016 

References

Eadie, G., Harris, W., & Widrow, L. M. 2015, ApJ, 806, 54Google Scholar
Eadie, G., Harris, W., & Springford, A. 2015, Joint Statistical Meetings Proceedings, in press.Google Scholar
Hernquist, L. 1990 ApJ, 356, 359Google Scholar
Johnson, D. & Soderblom, D. 1987 AJ 93, 4CrossRefGoogle Scholar
Little, B. & Tremaine, S. 1987, ApJ, 320, 493Google Scholar
Wang, W., Han, J., Cooper, A., Shaun, C., Frenk, C., & Lowing, B. 2015 MNRAS, 453, 377Google Scholar