Nonparametrical Statistical Methods for Astrophysical and Cosmological Data

天体物理和宇宙学数据的非参数统计方法

• 批准号: 0434343
• 负责人: Christopher Genovese
• 金额: $ 53.95万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-10-01 至 2007-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0434343&HistoricalAwards=false
• 关键词: Nonparametrical; Statistical; Methods; Astrophysical; Cosmological

AST-0434343GenoveseRecent technological advances have enabled astronomers and cosmologists to collect data of unprecedented quality and quantity. These large data sets can reveal more complex and subtle effects than ever before, but they also demand new statistical approaches. This project consists of two intertwined components: (a) development of new nonparametric statistical methods that address recurrent problems in the analysis of astrophysical and cosmological data and (b) application of the new methods to help answer significant astrophysical and cosmological questions. Specifically, this research will improve inference for the Cosmic Microwave Background spectrum by constructing uniform confidence sets in nonparametric regression, characterize the influence of local environment on galaxy evolution by developing new methods for nonparametric errors-in-variables problems, and estimate the matter density from magnitude limited galaxy surveys by producing accurate density estimators for doubly truncated data.The research provides interdisciplinary training for postdoctoral fellows and graduate students, and strengthens an interdisciplinary infrastructure between the mathematical and physical sciences.

最近的技术进步使天文学家和宇宙学家能够收集前所未有的质量和数量的数据。这些大型数据集可以揭示比以往任何时候都更复杂和微妙的影响，但它们也需要新的统计方法。 该项目由两个相互交织的部分组成：（a）开发新的非参数统计方法，解决天体物理学和宇宙学数据分析中经常出现的问题;（B）应用新方法帮助回答重要的天体物理学和宇宙学问题。 具体而言，本研究将通过在非参数回归中构造统一的置信集来改进宇宙微波背景谱的推断，通过开发非参数变量误差问题的新方法来表征局部环境对星系演化的影响，并通过对双截断数据产生精确的密度估计，从星等有限的星系巡天中估计物质密度。这项研究提供了跨学科的培训博士后研究员和研究生，并加强数学和物理科学之间的跨学科基础设施。