Development of Innovative Statistical Tools to Address Data-Analytic Challenges in Physics and Astronomy

开发创新统计工具来解决物理和天文学中的数据分析挑战

• 批准号: RGPIN-2021-03985
• 负责人: Stenning, David
• 金额: $ 1.31万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742803
• 关键词: Development; Innovative; Statistical; Tools; Address

Astronomy is fertile ground for high-impact statistical challenges. The overarching goal of my new research program is to advance astronomy by developing statistical methods that incorporate physics-based computer simulators, are suited to the particular scientific and data-analytic challenges at hand, and provide uncertainty quantification. This effort involves four objectives in two application areas. To be clear, the proposed program aims to develop statistical methods to address astronomy challenges but is not a physics proposal. Hunting for Earth-like Exoplanets in the Presence of Stellar Activity. A prized goal in astronomy is the discovery of Earth-like exoplanets. A complication is that most stars exhibit activity (e.g., star spots) that can mimic a planetary signal and lead to false detections. Objective 1 is to develop a framework for detecting Earth-like exoplanets orbiting Sun-like stars. This involves developing a model selection procedure to identify stellar activity models with the highest exoplanet detection power, while also using machine learning to derive data-driven stellar activity proxies and proposing flexible statistical models to capture them. Objective 2 adapts the framework to apply to host stars that differ substantially from the Sun, using hierarchical models to pool information across similar stars and thereby learn population-level stellar activity distributions. Computer Model Emulation and Calibration with Chemical Spectra. The ChemCam instrument on the Curiosity Rover obtains chemical spectra to learn about the composition of rocks and soils on Mars. Disaggregation, i.e., determining the composition of a target, is complicated by matrix effects-interactions between chemical compounds that amplify or suppress peaks in the observed spectrum. Objective 3 is to combine computer simulators with ChemCam's spectral data and Bayesian variable selection techniques to directly solve the disaggregation problem. A challenge is that a multi-compound simulator run takes hours on modern parallel computing platforms. Objective 4 aims to overcome this limitation by first constructing fast emulators of many single-compound simulators, then using a hierarchical model to combine the fitted emulators with a few runs of the multi-compound simulator. Billions of dollars are spent developing instruments such as exoplanet-hunting telescopes and Mars rovers; comprehending and analyzing the complex datasets generated by these instruments requires sophisticated statistical methodology that is lacking. This research program will provide astronomers the tools they need to accomplish high-impact goals such as confidently detecting Earth-like exoplanets or probing the history of Mars via the composition of its terrain. The program will support the training of highly qualified personnel (HQP), providing them with in-demand skills in statistics and machine learning, and will generally grow the presence of astrostatistics research in Canada.

天文学是应对高影响力统计挑战的沃土。我的新研究计划的总体目标是，通过开发纳入基于物理学的计算机模拟器的统计方法来推动天文学的发展，这些方法适合手头的特殊科学和数据分析挑战，并提供不确定性量化。这项工作涉及两个应用领域的四个目标。需要明确的是，拟议的计划旨在开发统计方法来应对天文学挑战，但并不是一个物理学建议。在有恒星活动的情况下寻找类地系外行星。天文学的一个重要目标是发现类似地球的系外行星。一个复杂的问题是，大多数恒星都表现出类似行星信号的活动(例如，恒星黑子)，从而导致错误探测。目标1是开发一个探测围绕类太阳恒星运行的类地系外行星的框架。这包括开发一个模型选择程序，以确定具有最高系外行星探测能力的恒星活动模型，同时还使用机器学习来得出数据驱动的恒星活动代理，并提出灵活的统计模型来捕获它们。目标2调整了该框架，使其适用于与太阳有很大不同的主恒星，使用分层模型来汇集类似恒星的信息，从而了解种群水平的恒星活动分布。用化学光谱进行计算机模型仿真和校正。好奇号火星车上的化学摄像头设备可以获取化学光谱，以了解火星上岩石和土壤的组成。解聚，即确定目标的组成，由于基质效应而变得复杂，基质效应是化合物之间的相互作用，放大或抑制观察到的光谱中的峰。目标3是将计算机模拟器与ChemCam的光谱数据和贝叶斯变量选择技术相结合，直接解决解聚问题。一个挑战是，在现代并行计算平台上运行多个复合模拟器需要几个小时。目标4旨在克服这一局限性，首先构建多个单组合模拟器的快速模拟器，然后使用分层模型将拟合的模拟器与多组合模拟器的几次运行相结合。数十亿美元被花费在开发系外行星搜索望远镜和火星漫游车等仪器上；理解和分析这些仪器产生的复杂数据集需要复杂的统计方法，而这一点是缺乏的。这项研究计划将为天文学家提供完成高影响目标所需的工具，例如自信地探测类似地球的系外行星，或通过火星地形的组成来探测火星的历史。该计划将支持对高素质人员(HQP)的培训，为他们提供统计和机器学习方面的需求技能，并将普遍扩大天体统计研究在加拿大的存在。