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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765789
• 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.

天文学是高影响力统计挑战的沃土。我的新研究计划的首要目标是通过开发统计方法来推进天文学，这些方法结合了基于物理的计算机模拟器，适合手头的特定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中的四个目标。需要明确的是，该计划旨在开发统计方法来应对天文学挑战，而不是物理学提案。在恒星活动中寻找类地系外行星。天文学的一个重要目标是发现类地系外行星。一个复杂的问题是，大多数恒星表现出的活动（例如，恒星黑子）可以模仿行星信号，导致错误的探测。目标1是开发一个框架，用于探测围绕类太阳恒星运行的类地系外行星。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派生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将该框架应用于与太阳有很大差异的主星，使用分层模型汇集类似恒星的信息，从而了解人口水平的恒星活动分布。化学光谱的计算机模型仿真与校准。好奇号火星车上的化学照相机获取化学光谱，以了解火星上岩石和土壤的组成。分解，即确定目标的组成，由于基质效应而变得复杂-化合物之间的相互作用会放大或抑制观察光谱中的峰。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，直接解决分解问题。一个挑战是，在现代并行计算平台上运行一个多复合模拟器需要几个小时。目标4旨在克服这一限制，首先构建多个单化合物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化合物模拟器的几次运行相结合。数十亿美元被用于开发诸如系外行星搜寻望远镜和火星探测器之类的仪器；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，而这是目前所缺乏的。这个研究项目将为天文学家提供他们完成高影响力目标所需的工具，比如自信地探测类地系外行星，或者通过火星地形的组成来探测火星的历史。该项目将支持高素质人才（HQP）的培训，为他们提供所需的统计学和机器学习技能，并将总体上增加加拿大天体统计学研究的存在。