Collaborative Research: Science-Aware Computational Methods for Accelerating Data-Intensive Discovery: Astroparticle Physics as a Test Case

协作研究：加速数据密集型发现的科学感知计算方法：天体粒子物理学作为测试用例

• 批准号: 1940080
• 负责人: Rudolf Eigenmann
• 金额: $ 33.33万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940080&HistoricalAwards=false
• 关键词: Collaborative; Research; Science; Aware; Computational

The rapid technological advances of the last two decades have ushered in an era of data-rich science for several disciplines. One such discipline is astroparticle physics, where researchers aim to discover what our Universe is made of by trying to directly detect Dark Matter. This discovery can be hastened if data science tools are used to extract significant domain-specific information from data, and to reliably test scientific hypotheses at scale. The overarching goal of this two-year project is to lay the groundwork for incorporating scientific knowledge into machine learning and data science methods in the context of scientific disciplines in which discovery requires effective, efficient analysis of lots of noisy data gathered by multiple imperfect sensors. In doing so, it not only advances the state-of-the-art in data science, machine learning, and astrophysics, but it also has the potential to accelerate data-driven discoveries in other scientific disciplines where data shares similar characteristics.This project will develop innovative domain-enhanced data science methods that will be based on probabilistic graphical models and graph-regularized inverse problems. Using the leading astroparticle experiment XENON as a test bed, the investigators will explore and demonstrate approaches for incorporating domain knowledge into machine learning and data science methods. In doing so, the investigators will address major data-analysis challenges in the context of dark matter identification. Additionally, the investigators will invest significant effort reaching out to other data-intensive science communities, such as materials science, oceanography, and meteorology, that can benefit from the new methods and ideas. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

过去二十年的快速技术进步为几个学科带来了一个数据丰富的科学时代。 其中一个学科是天体粒子物理学，研究人员的目标是通过直接探测暗物质来发现我们的宇宙是由什么组成的。如果使用数据科学工具从数据中提取重要的特定领域信息，并可靠地大规模测试科学假设，则可以加速这一发现。这个为期两年的项目的总体目标是为在科学学科的背景下将科学知识融入机器学习和数据科学方法奠定基础，其中发现需要有效，高效地分析多个不完美的传感器收集的大量噪声数据。通过这样做，它不仅推进了数据科学、机器学习和天体物理学的最新发展，而且有可能加速数据驱动的其他科学学科的发现，这些学科的数据具有相似的特征。该项目将开发基于概率图模型和图正则化逆问题的创新领域增强数据科学方法。使用领先的天体粒子实验XENON作为测试平台，研究人员将探索和演示将领域知识融入机器学习和数据科学方法的方法。在此过程中，研究人员将解决暗物质识别背景下的主要数据分析挑战。此外，研究人员将投入大量精力，接触其他数据密集型科学界，如材料科学，海洋学和气象学，这些科学界可以从新方法和想法中受益。该项目是美国国家科学基金会利用数据革命（HDR）大创意活动的一部分。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。