MSPA-AST: Multilevel Modeling of Active Galaxy Populations

MSPA-AST：活跃星系群的多级建模

• 批准号: 0908439
• 负责人: Thomas Loredo
• 金额: $ 66.86万
• 依托单位: Cornell 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 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0908439&HistoricalAwards=false
• 关键词: MSPA; AST; Multilevel; Modeling; Active

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Dr Loredo and his team will develop Bayesian multi-level modeling for astrophysical problems. Bayesian multi-level models decompose the random variation in measured quantities into distinct layers, each representing a different contribution: layers 'close' to the data might describe the uncertainty in the measurements, while layers 'farther away' typically describe cosmic scatter arising from unobserved variables. Their first project aims to account for two common sources of bias in estimating the distribution of luminosities within a sample of objects: bias from the way that the sample is selected, and bias stemming from the fact that the 'measured' brightnesses of objects are not in fact independent of each other. Astronomers often neglect the second of these, although it can be significant. Dr Loredo and his team will apply their methods to data from the Two-Degree Field quasar survey and the Sloan Digital Sky Survey. In their second project, the team will use a Bayesian multi-level model to test whether the active galactic nuclei seen near the directions from which ultra-high energy cosmic rays are observed are truly associated with the cosmic-ray sources, or whether the alignment is likely to be coincidental. The team will apply their methods to cosmic-ray measurements from HiRes and the Auger observatory. The project team includes experts in both astronomy and statistics. Dr Loredo is an associate of the Center for Astrostatistics at Penn State University, where he lectures at the annual Summer School. At that summer school, he will offer topical sessions on statistical methods for astronomical surveys and multilevel models. The research should produce significant innovations in both disciplines, and will improve the science return from large astronomical surveys.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。Loredo博士和他的团队将为天体物理问题开发贝叶斯多层次建模。 贝叶斯多层次模型将测量量的随机变化分解为不同的层，每个层代表不同的贡献：“接近”数据的层可能描述测量的不确定性，而“远离”的层通常描述由未观测到的变量引起的宇宙散射。 他们的第一个项目旨在解释在估计物体样本内的光度分布时两个常见的偏差来源：来自样本选择方式的偏差，以及来自物体的“测量”亮度实际上并不相互独立的事实的偏差。 天文学家经常忽略第二个，尽管它可能很重要。 Loredo博士和他的团队将把他们的方法应用于2度场类星体调查和斯隆数字巡天的数据。 在他们的第二个项目中，该团队将使用贝叶斯多层次模型来测试在观测到超高能宇宙射线的方向附近看到的活动星系核是否与宇宙射线源真正相关，或者这种对齐是否可能是巧合。 该团队将把他们的方法应用于HiRes和Auger天文台的宇宙射线测量。该项目小组包括天文学和统计学专家。 Loredo博士是宾夕法尼亚州立大学天体统计中心的助理，他在那里的年度暑期学校讲课。 在暑期学校，他将提供专题会议的统计方法，天文调查和多级模型。 这项研究应该会在这两个学科中产生重大的创新，并将提高大型天文调查的科学回报。