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 Clydein大型项目天文学家通常提出一个定义明确的问题，计划一组观察，获取所有数据，然后进行分析。 当观察资源稀缺且昂贵时，一种自适应方法，选择基于到目前为止的数据进行的观察值应该能够提高作品的准确性，精度和效率。 这项研究将开发一个基于迭代观察推动设计周期，依靠贝叶斯方法并称为贝叶斯自适应探索（BAE）的灵活，适应性科学探索的一般框架。 通过广泛的方法论研究开发BAE，然后将BAE应用于几个时间变化现象，将导致统计学中的许多理论和算法创新，并在我们的太阳系外行星，二进制恒星系统，我们的太阳系中的次要行星以及附近的银河系中可变星星的研究中显着改善答案。 由于所需的计算非常苛刻，因此将探索几种方法，包括扩展现有的马尔可夫链蒙特卡洛和顺序的蒙特卡洛推理算法，开发桥梁/路径重要性采样算法和高斯工艺元模型，以及对亚地区适应性quadrature.bae的使用。 该项目将使用教程和教学材料创建并广泛传播实施方法的公共域软件。 至少一名研究生将接受广泛的跨学科培训。