MSPA-AST: Searching for Earth-Mass Planets: Bayesian Algorithms to Analyze Transit Timing Observations

MSPA-AST：搜索地球质量行星：用于分析凌日定时观测的贝叶斯算法

• 批准号: 0707203
• 负责人: Eric Ford
• 金额: $ 25万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0707203&HistoricalAwards=false
• 关键词: MSPA; AST; Searching; Earth; Mass

AST-0707203FordExtrasolar planet searches have revealed an unexpected diversity of planetary systems and provided insights into the mechanisms of planet formation. Recent transit timing observations are very powerful for detecting Earth-mass planets around stars with transiting giant planets. However, relatively little attention has been paid to the statistical methods that will be necessary to make robust planet detections and accurate measurements of planets' masses and orbital properties. In this project, Dr. Ford and collaborators will work to provide the needed statistical foundation for transit timing observations to detect and characterize Earth-mass planets using existing ground-based observatories. Statistical and computational methods are particularly important because the transit timing signature of a low-mass planet is typically dominated by the mutual gravitational interactions between the low-mass and giant planet, rather than by the direct effect of the planets on the star. This requires computationally expensive N-body integrations of a highly non-linear system, which makes computationally efficient statistical methods essential. This program will research algorithms for rapidly exploring high dimensional parameter spaces with an adaptive surrogate Bayesian model, and for efficiently calculating Bayes factors from posterior samples generated via Markov chain Monte Carlo. It can be expected to lead to robust detections of low-mass planets and constraints on planet formation theory.This research will contribute general statistical algorithms to the public domain. They will be applicable to other types of current and future extrasolar planet searches as well as a broad range of problems in time series analysis and Bayesian model comparison. Through this project, a postdoctoral research associate and graduate and undergraduate students will develop extensive expertise in both statistics and astrophysics by participating in cutting edge research. An astrostatistics visitor program, including experts from underrepresented groups, will train a broader array of students in modern statistical methods for the physical sciences and will stimulate interaction and collaboration of astronomers and physicists with statisticians.

AST-0707203FordExtrasolar Planet搜索揭示了行星系统的意外多样性，并提供了对行星形成机制的见解。 最近的过境时序观测非常有力，可用于检测具有过境巨型行星的恒星周围的地球质量行星。 但是，对于对行星质量和轨道特性进行稳健的行星探测和准确测量所必需的统计方法的关注很少。 在这个项目中，福特博士和合作者将努力为使用现有的基于地面的观测值来检测和表征地球质量行星所需的统计基础。 统计和计算方法尤其重要，因为低质量行星的传输时序特征通常由低质量和巨型行星之间的相互重力相互作用主导，而不是行星对恒星的直接影响。 这需要高度非线性系统的计算昂贵的N体集成，这使计算有效的统计方法必不可少。 该程序将研究使用自适应替代贝叶斯模型快速探索高维参数空间的算法，并有效地从Markov Chain Monte Carlo生成的后验样品中有效计算贝叶斯因子。 可以预期，这将导致对行星形成理论的低质量行星和约束。这项研究将为公共领域贡献一般的统计算法。 它们将适用于其他类型的当前和未来的外星行星搜索，以及时间序列分析和贝叶斯模型比较的广泛问题。 通过该项目，博士后研究助理，研究生和本科生将通过参与尖端研究来发展统计和天体物理学方面的广泛专业知识。 一项Astrostatistics访问者计划，包括来自代表性不足的团体的专家，将培训更多有关物理科学的现代统计方法的学生，并将刺激天文学家和物理学家与统计学家的互动和协作。