CyberTraining: Implementation: Small: Enabling Dark Matter Discovery through Collaborative Cybertraining

网络培训：实施：小型：通过协作网络培训实现暗物质发现

• 批准号: 2017506
• 负责人: Andrew Renshaw
• 金额: $ 16.21万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017506&HistoricalAwards=false
• 关键词: CyberTraining; Implementation; Small; Enabling; Dark

Detecting dark matter in the lab would be transformational for physics, and such a difficult measurement requires providing a foundation for early-career scientists in advanced data analytics. The science question being pursued is generally acknowledged to be one of the most important questions in particle physics and astrophysics and is key to understanding what makes up the vast majority of the universe. Effective training in good computing practices is required for major research advances in this field. The project will consolidate and strengthen training efforts in scientific software development and data analysis within the field of experimental dark matter research. Scientifically, the training will enable discovery that will come from a world-wide effort consisting of hundreds of junior scientists searching for extremely-rare events on petabytes of data - effectively looking for a needle in a haystack the size of Texas. The project serves the national interest as stated by NSF's mission to promote the progress of science by preparing a workforce trained in cyberinfrastructure, and will support STEM disciplines with critical software training that is much needed both in scientific fields and in industry.The dark matter community consists of more than a thousand scientists at the frontier of ultra-rare event searches whose efforts support more than twenty different experiments. Searching for dark matter in multiple ways has resulted in disparate and often inadequate computational training. This project addresses the training problem to maximize impact across the field. Representing three leading dark matter experiments, the project investigators will develop educational material and training workshops for systematic data science education to ensure early career scientists can harness the data volumes being produced by modern experiments. The project will host two training workshops per year, toward the goal of developing a community of instructors and also a set of training materials for free distribution and reuse. Beyond domain-specific training in rare-event searches, foundational computational knowledge will be developed when necessary by working with partners such as the Software and Data Carpentries. The project includes specific goals to engage women and underrepresented minorities in the training activities and broaden their advancement within the field. Additionally, the project will provide mentors for advanced students through hackathons. These trainings will directly contribute to broader STEM workforce development while training students such that they can pursue careers in data science and/or data-intensive research. This project is funded by the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering and the Division of Physics in the Directorate for Mathematical and Physical Sciences.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.

在实验室中检测暗物质将给物理学带来变革，而如此困难的测量需要为早期职业科学家提供高级数据分析的基础。 所追求的科学问题被普遍认为是粒子物理学和天体物理学中最重要的问题之一，也是理解宇宙绝大多数组成部分的关键。 该领域的重大研究进展需要良好计算实践的有效培训。 该项目将巩固和加强实验暗物质研究领域内科学软件开发和数据分析的培训工作。 从科学角度来看，培训将促成世界范围内数百名初级科学家的共同努力，在数 PB 的数据上寻找极其罕见的事件，有效地在德克萨斯州大小的大海捞针中寻找线索。 该项目服务于 NSF 使命所规定的国家利益，即通过培养受过网络基础设施培训的劳动力来促进科学进步，并将通过科学领域和工业界急需的关键软件培训来支持 STEM 学科。暗物质社区由一千多名处于超罕见事件搜索前沿的科学家组成，他们的努力支持二十多个不同的实验。 以多种方式寻找暗物质导致了不同且往往不充分的计算训练。该项目解决了培训问题，以最大限度地提高整个领域的影响力。 项目研究人员代表三个领先的暗物质实验，将为系统数据科学教育开发教材和培训研讨会，以确保早期职业科学家能够利用现代实验产生的数据量。 该项目每年将举办两次培训研讨会，旨在发展一个讲师社区以及一套免费分发和重复使用的培训材料。除了稀有事件搜索领域的特定培训之外，必要时还将与 Software 和 Data Carpentries 等合作伙伴合作开发基础计算知识。 该项目包括让妇女和代表性不足的少数群体参与培训活动并扩大其在该领域内进步的具体目标。 此外，该项目还将通过黑客马拉松为高级学生提供导师。这些培训将直接促进更广泛的 STEM 劳动力发展，同时培训学生，使他们能够从事数据科学和/或数据密集型研究的职业。 该项目由计算机和信息科学与工程理事会高级网络基础设施办公室以及数学和物理科学理事会物理部资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。