Collaborative Research: Principled Science-Driven Methods for Massive, Intricate, and Multifaceted Data in Astronomy and Astrophysics

协作研究：天文学和天体物理学中海量、复杂和多方面数据的原则性科学驱动方法

• 批准号: 1513484
• 负责人: Thomas Chun Man Lee
• 金额: $ 8.75万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513484&HistoricalAwards=false
• 关键词: Collaborative; Research; Principled; Science; Driven

Massive new data resources are coming online in every area of human exploration, changing the way researchers approach data analysis. All-purpose algorithms are expected to identify patterns in data with little tailoring to the problem at hand. While this all-purpose approach is understandable given the massive computational challenges of "big data," it often sacrifices our ability to understand underlying scientific processes. On the other hand, explicitly embedding scientific models into massive statistical analyses may pose significant computational challenges. This project navigates the tension between such all-purpose or "data-driven" methods and specially-designed or "science-driven" methods using a suite of data analytic projects in astro- and solar physics. This work is motivated by recent advances in space-based instrumentation that are increasing both the quality and the quantity of data available to astronomers. Several projects focus on developing new methods for detecting and characterizing astronomical sources, combining information in observations made across the electromagnetic spectrum, including high resolution spectrography, imaging, and time series. Other projects investigate methods for extracting useful features from ultra-high-resolution images of the Sun with the ultimate aim of predicting explosive dynamic processes in the solar atmosphere. The CHASC International Center for Astrostatistics has a track record of designing methods that leverage efficient data-driven techniques but still incorporate scientific understanding of the astronomical sources and maintain the ability to answer specific scientific questions about the underlying astronomical and physical processes. The CHASC Center not only aims to develop new methods for astronomy but also plans to use these problems as springboards in the development of new general statistical methods, especially in signal processing, image analysis, multilevel modeling, and computational statistics.The CHASC International Center for Astrostatistics plans to tackle these challenges using principled statistical methods that incorporate both data-driven and science-driven approaches. For example, the investigators will use coarse data-driven models in an initial analysis that aims to identify simple structures that can be used in a more scientifically meaningful secondary analysis. To formally test for unexpected features in astrophysical images, the team will use flexible data-driven models for deviations from science-driven models for known features. As these examples illustrate, modern astrostatistical analyses involve subtle tradeoffs between complexity and practicality and pose significant computational challenges. A primary aim of this project is to produce tailored Monte Carlo methods that are efficient in such complex settings. The team's statisticians (Meng, van Dyk, Lee, and Stein) have substantial research experience in developing the methods that the Center will extend, employ, and publicize to tackle these challenges: inferential and efficient computational methods under highly-structured models that involve multi-scale structure and/or multiple levels of latent variables and incomplete data. Such models are ideally suited to account for the many physical and instrumental filters of the data generation mechanisms in astrophysics. The astronomers (Kashyap and Siemiginowska) have expertise in the instrumentation and science of high-energy and optical astronomy, and have collaborated with statisticians in developing methods to address scientific questions. It is expected that a fundamental impact of this research will be more general acceptance and use of appropriate methods among astronomers. Second, the development of methods for efficient modeling of scientific phenomena, the comparison of complex models, and science-driven classification and clustering will help solve complex data analytic challenges throughout the natural, social, medical, and engineering sciences.

大量的新数据资源正在人类探索的每个领域进行在线，改变了研究人员对数据分析的方式。预计通用算法将识别数据中的模式，而几乎没有针对手头的问题剪裁。鉴于“大数据”的巨大计算挑战，这种通用方法是可以理解的，但它经常牺牲我们理解基本科学过程的能力。另一方面，将科学模型明确嵌入大规模的统计分析中可能会带来重大的计算挑战。该项目可以使用Astro和太阳能物理学中的一系列数据分析项目来导航这种通用或“数据驱动”方法与专门设计或“科学驱动”方法之间的张力。这项工作是由太空仪器的最新进展激发的，这些仪器正在增加天文学家可用的数据质量和数量。几个项目着重于开发用于检测和表征天文来源的新方法，将信息结合在整个电磁频谱的观察结果中，包括高分辨率光谱学，成像和时间序列。其他项目研究了从太阳超高分辨率图像中提取有用特征的方法，其最终目的是预测太阳大气中的爆炸性动态过程。 CHASC国际天线列表中心具有设计方法的往绩记录，该方法利用有效的数据驱动技术，但仍纳入了对天文学来源的科学理解，并保持了关于基本天文和物理过程的特定科学问题的能力。 Chasc Center不仅旨在开发新的天文学方法，还计划将这些问题用作跳板，以开发新的一般统计方法，尤其是在信号处理，图像处理，图像分析，多级建模和计算统计中。 例如，研究人员将在初始分析中使用粗糙的数据驱动模型，该模型旨在识别可以在更科学意义的二级分析中使用的简单结构。为了正式测试天体物理图像中意外的功能，该团队将使用灵活的数据驱动模型来偏离科学驱动的模型的已知功能。如这些示例所示，现代的天线分析涉及复杂性与实用性之间的微妙折衷，并带来了重大的计算挑战。该项目的主要目的是生产量身定制的蒙特卡洛方法，这些方法在这种复杂的环境中有效。该团队的统计学家（Meng，Van Dyk，Lee和Stein）在开发中心将扩展，采用和宣传以应对这些挑战的方法方面具有丰富的研究经验：在高度结构的模型下，推理和高效的计算方法涉及多尺度结构和/或多个级别的潜在变量和多个级别的变量和不满数据。这样的模型非常适合说明天体物理学数据生成机制的许多物理和工具过滤器。天文学家（Kashyap和Siemiginowska）在高能量和光学天文学的仪器和科学方面具有专业知识，并与统计学家合作开发了解决科学问题的方法。预计这项研究的基本影响将是天文学家中适当的方法的普遍接受和使用。其次，开发有效建模的科学现象，复杂模型的比较以及科学驱动的分类和聚类的方法将有助于解决整个自然，社会，医学和工程科学的复杂数据分析挑战。