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.

大量新数据资源正在人类探索的各个领域上线，改变了研究人员进行数据分析的方式。通用算法有望识别数据中的模式，而无需针对当前问题进行调整。虽然考虑到“大数据”带来的巨大计算挑战，这种通用方法是可以理解的，但它往往会牺牲我们理解底层科学过程的能力。另一方面，将科学模型明确嵌入大规模统计分析中可能会带来重大的计算挑战。该项目使用天体和太阳物理学中的一套数据分析项目来解决这种通用或“数据驱动”方法与专门设计或“科学驱动”方法之间的紧张关系。这项工作的动力来自天基仪器的最新进展，这些进展提高了天文学家可用数据的质量和数量。几个项目专注于开发检测和表征天文源的新方法，结合整个电磁频谱的观测信息，包括高分辨率光谱、成像和时间序列。其他项目研究从太阳超高分辨率图像中提取有用特征的方法，最终目标是预测太阳大气中的爆炸动态过程。 CHASC 国际天体统计中心在设计方法方面有着良好的记录，这些方法利用了高效的数据驱动技术，但仍然融入了对天文来源的科学理解，并保持了回答有关潜在天文和物理过程的具体科学问题的能力。 CHASC 中心不仅旨在开发新的天文学方法，还计划将这些问题作为开发新的通用统计方法的跳板，特别是在信号处理、图像分析、多级建模和计算统计方面。 CHASC 国际天体统计中心计划使用结合数据驱动和科学驱动方法的原则性统计方法来应对这些挑战。 例如，研究人员将在初步分析中使用粗略的数据驱动模型，旨在识别可用于更具科学意义的二次分析的简单结构。为了正式测试天体物理图像中的意外特征，该团队将使用灵活的数据驱动模型来消除已知特征的科学驱动模型的偏差。正如这些例子所示，现代天体统计分析涉及复杂性和实用性之间的微妙权衡，并带来了巨大的计算挑战。该项目的主要目标是产生在如此复杂的环境中有效的定制蒙特卡罗方法。该团队的统计学家（Meng、van Dyk、Lee 和 Stein）在开发该中心将扩展、采用和宣传的方法来应对这些挑战方面拥有丰富的研究经验：涉及多尺度结构和/或多层次的潜在变量和不完整数据的高度结构化模型下的推理和高效计算方法。此类模型非常适合解释天体物理学中数据生成机制的许多物理和仪器过滤器。天文学家（卡什亚普和西米吉诺斯卡）拥有高能和光学天文学的仪器和科学方面的专业知识，并与统计学家合作开发解决科学问题的方法。预计这项研究的根本影响将是天文学家更普遍地接受和使用适当的方法。其次，开发科学现象的有效建模、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自然、社会、医学和工程科学领域的复杂数据分析挑战。