Collaborative Research: Adaptive Experimental Design for Astronomical Exploration

协作研究：天文探索的自适应实验设计

• 批准号: 0507481
• 负责人: Merlise Clyde
• 金额: $ 26.58万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-15 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0507481&HistoricalAwards=false
• 关键词: Collaborative; Research; Adaptive; Experimental; Design

AST-0507481ClydeIn large projects astronomers typically pose a well-defined question, plan a set of observations, take all the data, and then analyze it. When observational resources are scarce and expensive, an adaptive approach to selecting which observations to make, based on the data assembled thus far, should be able to improve the accuracy, precision and efficiency of the work. This research will develop a general framework for flexible, adaptive scientific exploration based on iterating an Observation-Inference-Design cycle, relying on Bayesian methods and called Bayesian Adaptive Exploration (BAE). Developing BAE through a broad range of methodological research, and then applying BAE to several time-variable phenomena, will lead to numerous theoretical and algorithmic innovations in statistics, and to significantly improved answers in the study of extra-solar planets, binary star systems, minor planets in our solar system, and variable stars in nearby galaxies. Since the required calculations are extremely demanding, several approaches will be explored, including extending existing Markov Chain Monte Carlo and sequential Monte Carlo inference algorithms, developing bridge/path importance sampling algorithms and Gaussian process meta-models, and the use of sub-region-adaptive quadrature.BAE is an adaptive generalization of the scientific method and will therefore be broadly applicable in many disciplines. This project will create and widely disseminate public-domain software implementing the methods, with tutorial and instructional material. At least one graduate student will receive extensive interdisciplinary training.

AST-0507481克莱德在大型项目中，天文学家通常会提出一个定义明确的问题，计划一组观测，获取所有数据，然后进行分析。当观测资源稀缺且昂贵时，根据迄今为止收集的数据，选择进行哪些观测的自适应方法应该能够提高工作的准确性、精确度和效率。 这项研究将开发一个灵活的，自适应的科学探索的基础上迭代的观察-推理-设计周期，依赖于贝叶斯方法，称为贝叶斯自适应探索（BAE）的一般框架。 通过广泛的方法学研究开发BAE，然后将BAE应用于几种时变现象，将导致统计学中的许多理论和算法创新，并在太阳系外行星，双星星星系统，太阳系中的小行星和附近星系中的变星的研究中显着改进答案。 由于所需的计算是非常苛刻的，几种方法将被探索，包括扩展现有的马尔可夫链蒙特卡罗和顺序蒙特卡罗推理算法，开发桥/路径重要性采样算法和高斯过程元模型，并使用子区域自适应quadrature.BAE是一个自适应的科学方法的推广，因此将广泛适用于许多学科。 该项目将创建和广泛传播实施这些方法的公共领域软件，并提供教程和教学材料。 至少有一名研究生将接受广泛的跨学科培训。