MSPA-AST: Advancing Statistical Methodology in Massive Astronomical Surveys

AST-0707833Babu这是一套跨学科、多研究者、多方面的天文统计研究项目，连接了天文学和统计学领域。目前和未来的天文研究涉及大量的成像、光度和光谱调查，这些调查产生了TB到PB的数据库。虽然科学前景是巨大的，但实现这些目标关键取决于从如此庞大的数据集中提取有用知识的能力。统计问题多种多样，由于培训有限和方法学研究不够集中，天文学家通常面临着先进方法知识不足的问题。该项目最初的计划包括一系列活动，包括似然和重采样方法、光度红移的多变量方法、多变量数据集的局部分类、使用高效计算算法分析巨型数据集以及低分辨率光谱的分类。教育和传播工作仍然是工作的一大部分，包括继续组织每年一次的统计推断强化暑期班，该奖项不提供支持。然而，所有这些工作都将对天文测量科学的统计质量和许多年轻研究人员的培训产生重大影响。

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.