MSPA-AST: Advancing Statistical Methodology in Massive Astronomical Surveys

MSPA-AST：推进大规模天文调查中的统计方法

• 批准号: 0707833
• 负责人: Gutti Babu
• 金额: $ 10万
• 依托单位: Pennsylvania State Univ University Park
• 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 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0707833&HistoricalAwards=false
• 关键词: MSPA; AST; Advancing; Statistical; Methodology

AST-0707833BabuThis is a cross-disciplinary, multi-investigator, multi-faceted suite of astro-statistical research projects bridging the fields of astronomy and statistics. Current and future astronomical research involves vast imaging, photometric and spectroscopic surveys that produce terabyte to petabyte databases. While the scientific promise is tremendous, achieving the goals depends critically on the ability to extract useful knowledge from such large datasets. The statistical problems are diverse, and astronomers typically confront them with inadequate knowledge of advanced methods, due both to limited training and to insufficiently focused methodological research.The original plan for this project included a large range of activities, covering likelihood and resampling methods, multivariate methods for photometric redshifts, the local classification of multivariate datasets; analysis of mega-datasets with efficient computational algorithms; and classification of low-resolution spectra. The project as supported will cover a rather smaller subset of these topics, to be determined as the work progresses.The education and dissemination work remains a large fraction of the effort, including organizing the continuation of an annual intensive Summer School in statistical inference, not supported by this award. All of this work will, however, have a substantial impact on both the statistical quality of astronomical survey science and on the training of many young researchers.

AST-0707833333333BABUTHIS是一个跨学科的，多侵略的人，多方面的套件，由天文学和统计数据桥接。 当前和未来的天文学研究涉及大量成像，光度和光谱调查，这些调查可为PBABYTE数据库产生TBYTETE。 尽管科学的承诺是巨大的，但实现目标取决于从如此大的数据集中提取有用知识的能力。 统计问题是多种多样的，天文学家通常会因培训有限和专注的方法论研究而面临的高级方法知识不足。该项目的原始计划包括各种活动，涵盖了可能性和重新采样方法，多变量方法的多数分类，用于多次redshifts的多元分类，是多型多型数据的分类;用有效的计算算法分析大型数据集；和低分辨率光谱的分类。 所支持的项目将涵盖这些主题的较小子集，并随着工作的进行而确定。教育和传播工作仍然很大一部分，包括组织统计推断的年度强化暑期学校的延续，不受此奖项的支持。 但是，所有这些工作都将对天文学调查科学的统计质量和许多年轻研究人员的培训都有重大影响。