2014 Summer School in Statistics for Astronomers, June 2-13, 2014

2014 年天文学家统计暑期学校，2014 年 6 月 2-13 日

• 批准号: 1418179
• 负责人: Gutti Babu
• 金额: $ 4.8万
• 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: 2014
• 资助国家: 美国
• 起止时间: 2014-04-15 至 2016-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1418179&HistoricalAwards=false
• 关键词: 2014; Summer; Statistics; Astronomers; June

2014 Decennial Summer School in Statistics for Astronomers; June 2-13 2014; Pennsylvania State University, University Park, State College, PAPenn State's very popular astrostatistics summer school has trained over 575 total participants since its inauguration in 2005. If maintained at a steady state, this activity will train about 10% of the nation's young astronomers, providing critical skills to the US scientific workforce. Oriented towards graduate students and young researchers, participants receive an intense immersion in statistical methodology taught by highly skilled instructors. The three-pronged curriculum provides instruction in the underlying principles of modern statistics, exposure to advanced methodologies useful in astronomy, and hands-on training in statistical software. Attendees come away with a much heightened expertise in statistics and its applications to their science, a better understanding of how to teach this material to others, and a fine appreciation for the value and meaning of statistics in science. The instructors have devoted considerable effort to adapting the course material to their audience, and the curriculum continues to evolve. The formal textbook based on this curriculum was published in 2012.Astronomical research often involves imaging, photometric and spectroscopic surveys of the sky that produce tera- and peta-byte databases and billion-object catalogs. Survey science is the astronomy of the near future, with a new generation of formidable telescopes and the federation of diverse astronomical datasets. While the promise is great, achieving the scientific goals depends critically on extraction of useful knowledge using statistical inference, and especially the use of advanced statistical methods. Observational astronomers are thus confronting a wider range of statistical challenges than ever before, while unfortunately most U.S. astronomers are not well trained in statistics, learning only elementary methods through books written by and for physical scientists. Such volumes usually treat only a narrow range of problems, providing inadequate conceptual foundations in mathematical statistics and little guidance in advanced applied statistics. Statistics is an available technology that must be tapped to advance the needs of astronomy and astrophysics.The 2014 Summer School in statistical inference for young astronomers will present concepts and methodologies at an intermediate level, using experienced instructors and an innovative curriculum. This being the decennial offering, and based on comments from the past school participants, the 2014 school covers a two-week extended period of instruction, divided into three independent modules: the regular statistical inference course for astronomers in the first week, a two-day supplement on 'Statistical modeling of cosmic populations', and a three-day school on 'High-Performance Computing for Astronomical Data Analysis & Bayesian Computing'. The Penn State Research Computing & Cyberinfrastructure Group, and its Institute for CyberScience, are providing technical manpower and partially supporting the high-performance computing.

2014年夏季学校的天文学家统计学； 2014年6月2日至13日；宾夕法尼亚州立大学，大学公园，州立学院，帕佩恩州立大学非常受欢迎的天文学家暑期学校自2005年就职以来就已经培训了575多名参与者。如果保持稳定状态，该活动将培训该国约10％的年轻天文学家，为美国科学劳动力提供关键技能。 参与者面向研究生和年轻的研究人员，对高技能讲师教授的统计方法学深刻沉浸。 三管齐下的课程提供了现代统计基本原则，对天文学有用的先进方法的基本原则的指导以及统计软件中的动手培训。 与会者在统计数据及其对科学的应用方面具有更高的专业知识，更好地理解如何向他人讲授这些材料，并对科学统计的价值和含义表示良好的赞赏。 讲师致力于将课程材料调整为听众，课程不断发展。 基于此课程的正式教科书于2012年出版。媒介研究通常涉及产生TERA和PETA-BYTE数据库以及数十亿个对象目录的天空的成像，光度和光谱调查。 调查科学是不久的将来的天文学，新一代强大的望远镜和各种天文学数据集的联合会。 尽管承诺是巨大的，但实现科学目标取决于使用统计推断，尤其是使用先进的统计方法来提取有用的知识。 因此，观察性天文学家比以往任何时候都面临着更广泛的统计挑战，虽然大多数美国天文学家在统计学方面没有接受过良好的培训，但仅通过撰写和物理科学家编写的书籍学习基本方法。 这样的卷通常仅处理狭窄的问题，从而提供数学统计数据中的概念基础不足，而高级应用统计中的指导很少。 统计数据是一项可用的技术，必须采用以促进天文学和天体物理学的需求。2014年夏季对年轻天文学家的统计推断将使用经验丰富的教师和创新的课程提出概念和方法。 This being the decennial offering, and based on comments from the past school participants, the 2014 school covers a two-week extended period of instruction, divided into three independent modules: the regular statistical inference course for astronomers in the first week, a two-day supplement on 'Statistical modeling of cosmic populations', and a three-day school on 'High-Performance Computing for Astronomical Data Analysis & Bayesian Computing'. 宾夕法尼亚州立研究计算与网络基础设施小组及其网络科学研究所正在提供技术人力，并部分支持高性能计算。