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

运动学数据的动力学模型：银河系盘和光环

• 批准号: 0908357
• 负责人: David Hogg
• 金额: $ 14.7万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0908357&HistoricalAwards=false
• 关键词: Dynamical; Models; Kinematic; Data; Milky

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.

霍格博士将使用现代统计方法研究银河系中恒星的观测运动。 其目的是研究如何最好地利用欧洲航天局的一项使命盖亚的预期数据返回，以约束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。 霍格博士将应用贝叶斯统计学来联合收割机组合不同的数据集，将它们与可以处理具有较大观测误差的数量的算法相结合。 他将通过获得太阳附近银河系盘表面质量密度的改进估计来测试这些技术。一名研究生将通过参与研究进行培训，包括国际研究经验。 所开发的软件将作为开放源代码发布。 霍格博士将为本科生和研究生构建问题集，教程，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。