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

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。通用算法被期望识别数据中的模式，而很少针对手头的问题进行定制。虽然这种通用的方法是可以理解的，因为“大数据”的巨大计算挑战，它往往牺牲了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到大规模统计分析中可能会带来重大的计算挑战。该项目使用一套天文和太阳物理学的数据分析项目来驾驭这种通用或"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。这项工作的动机是天基仪器的最新进展，这些进展提高了天文学家可获得的数据的质量和数量。有几个项目侧重于开发探测和表征天文源的新方法，将电磁波谱观测中的信息结合起来，包括高分辨率光谱学、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，最终目的是预测太阳大气层中的爆炸动态过程。CHASC国际天体统计中心在设计方法方面有着良好的记录，这些方法利用了有效的数据驱动技术，但仍然结合了对天文学来源的科学理解，并保持了回答有关基本天文学和物理过程的具体科学问题的能力。CHASC中心不仅致力于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，旨在确定可用于更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确定已知特征与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，并提出了重大的计算挑战。该项目的主要目的是生产量身定制的Monte Carlo方法，在这种复杂的环境中是有效的。该团队的统计学家（Meng，货车Dyk，Lee和Stein）在开发该中心将扩展，使用和宣传的方法以应对这些挑战方面具有丰富的研究经验：在涉及多尺度结构和/或多层次潜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。这种模型非常适合于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（Kashyap和Siemiginowska）在高能和光学天文学的仪器和科学方面具有专门知识，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，科学现象的有效建模方法的开发，复杂模型的比较，以及科学驱动的分类和聚类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。

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.