Dynamical Models from Kinematic Data: The Milky Way Disk and Halo

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Dr Hogg will study the observed motions of stars in the Milky Way using modern statistical methods. The aim is to investigate how best to use the expected data return from Gaia, a mission of the European Space Agency, to constrain the mass distribution of the Milky Way and its formation history. Current models assume that the Milky Way is in a steady state, and many require it to be axisymmetric as well. Dr Hogg will apply Bayesian statistics to combine disparate data sets, combining them with algorithms that can handle quantities with large observational errors. He will test these techniques by obtaining an improved estimate of the surface mass density of the Milky Way's disk near the Sun.A graduate student will be trained through participation in the research, including an international research experience. The software developed will be released as open-source code. Dr Hogg will construct problem sets, worksheets, tutorials, and online exercises in applying Bayesian inference to simple dynamics problems, for undergraduate and beginning-graduate students. This award is made jointly by NSF's Astronomical Sciences Division and its Office of International Science and Engineering.