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

霍格博士将用现代统计方法研究观测到的银河系中恒星的运动。其目的是研究如何最好地利用欧洲航天局（European Space Agency）的一项任务——盖亚（Gaia）发回的预期数据，来限制银河系的质量分布及其形成历史。目前的模型假设银河系处于稳定状态，许多模型也要求它是轴对称的。Hogg博士将运用贝叶斯统计来结合不同的数据集，并将它们与能够处理大量观测误差的算法结合起来。他将通过对靠近太阳的银河系圆盘表面质量密度的改进估计来测试这些技术。研究生将通过参与研究（包括国际研究经验）来接受培训。开发的软件将作为开源代码发布。Hogg博士将为本科生和研究生初学者构建问题集、工作表、教程和在线练习，将贝叶斯推理应用于简单的动力学问题。该奖项由美国国家科学基金会天文科学部及其国际科学与工程办公室联合颁发。

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.