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

在人类探索的各个领域，大量新的数据资源正在上线，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，几乎不需要为手头的问题量身定做。尽管考虑到“大数据”带来的巨大计算挑战，这种万能的方法是可以理解的，但它往往会牺牲我们理解潜在科学过程的能力。另一方面，将科学模型明确地嵌入到大规模统计分析中可能会带来巨大的计算挑战。这个项目利用一套天文和太阳物理学的数据分析项目，在这种通用的或“数据驱动”的方法和专门设计的或“科学驱动的”方法之间的紧张关系中导航。这项工作是由天基仪器的最新进展推动的，这些进展正在提高天文学家可获得的数据的质量和数量。几个项目的重点是开发新的方法来探测和表征天文信号源，将电磁光谱观测中的信息结合起来，包括高分辨率光谱分析、成像和时间序列。其他项目研究从太阳的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中的爆炸性动态过程。天文中心国际天体统计中心在设计方法方面有过往记录，这些方法利用有效的数据驱动技术，但仍然纳入对天文来源的科学理解，并保持回答有关基本天文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，而且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、多水平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的原则性统计方法来应对这些挑战。例如，调查人员将在初始分析中使用粗略的数据驱动模型，旨在确定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偏离已知特征的科学驱动模型。正如这些例子所表明的那样，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。这个项目的一个主要目标是产生在这样复杂的环境中高效的量身定制的蒙特卡罗方法。该团队的统计学家(Meng、van Dyk、Lee和Stein)在开发中心将扩展、使用和宣传以应对这些挑战的方法方面拥有丰富的研究经验：涉及多尺度结构和/或多级别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。这样的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)在高能天文学和光学天文学的仪器和科学方面拥有专门知识，并与统计学家合作制定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广泛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，将有助于解决整个自然、社会、医学和工程科学中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.