Statistical Methodology in Astronomy

天文学统计方法

• 批准号: 0071681
• 负责人: John Rice
• 金额: $ 25.02万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0071681&HistoricalAwards=false
• 关键词: Statistical; Methodology; Astronomy

ABSTRACTProfessor Rice proposes research on statistical problems arising in two large astronomical projects: the variable star database of the MACHO project (a search for dark matter in the halo of our galaxy) and Taiwanese American Occultation Survey (TAOS), a search for comets in the Kuiper belt. The first involves statistical characterization and modeling of tens of thousands of light curves from variable stars. He proposes to approach these problems from the viewpoint of functional data analysis, further developing and extending the methodology of this rapidly growing area of statistical research. The TAOS project will monitor star fields for occultations by objects in the Kuiper belt, using dedicated telescopes in the interior mountains of Taiwan. The primary statistical problems center around designing image processing and signal detection procedures that will operate in real time at high sampling rates to detect rare and faint signals. Both projects are anticipated to make valuable contributions to both advancement of knowledge in astronomy and to the development of statistical methodology.This is an interdisciplinary proposal involving mathematical statistics and astronomy, centering around two projects in astronomy: (1) The analysis of a large database of variable stars. These are stars whose light is not constant, but changes in a periodic fashion. Better understanding of this population of stars is important for models of stellar evolution and also for determining distances to remote objects in the universe. (2) The TAOS project will probe our solar system in the remote region beyond the orbit of Neptune. It is thought that there may well be hundreds of millions of objects such as comets there, but because of their relatively small size and remoteness, they are very difficult to detect. Computationally intensive statistical methodology will play a key role in detecting these objects. Partial funding for this project was provided by the Stellar Astronomy and Astrophysics (SAA) Program.

摘要Rice教授建议研究两个大型天文项目中出现的统计问题：MACHO项目的变星数据库（寻找银河系光晕中的暗物质）和台美掩星调查（TAOS），寻找柯伊伯带中的彗星。第一种方法是对成千上万的变星光曲线进行统计表征和建模。他建议从功能数据分析的角度来解决这些问题，进一步发展和扩展这一快速发展的统计研究领域的方法论。TAOS项目将使用位于台湾内陆山区的专用望远镜，监测柯伊伯带天体的掩星场。主要的统计问题集中在设计图像处理和信号检测程序，这些程序将以高采样率实时运行，以检测罕见和微弱的信号。预期这两个项目将对提高天文学知识和发展统计方法作出宝贵贡献。这是一项涉及数理统计和天文学的跨学科提案，主要围绕天文学中的两个项目：(1)分析一个大型变星数据库。这些恒星的光不是恒定的，而是以周期性的方式变化。更好地了解这群恒星对恒星演化模型和确定宇宙中遥远物体的距离都很重要。TAOS项目将在海王星轨道之外的遥远区域探测我们的太阳系。据认为，那里可能有数亿颗彗星之类的天体，但由于它们相对较小且地处偏远，很难被探测到。计算密集的统计方法将在检测这些目标方面发挥关键作用。本项目的部分资金由恒星天文学和天体物理学（SAA）计划提供。