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.

在实验室中探测暗物质对物理学来说将是一场变革，而如此困难的测量需要为从事高级数据分析的早期科学家提供基础。人们普遍认为，正在探索的科学问题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。为了在这一领域取得重大研究进展，需要进行有效的良好计算做法培训。该项目将巩固和加强在实验暗物质研究领域内科学软件开发和数据分析方面的培训工作。从科学上讲，这项培训将使全球范围内的发现成为可能，这项工作由数百名初级科学家组成，他们在数PB的数据中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。NSF的使命是通过培养一支接受过网络基础设施培训的劳动力来促进科学进步，该项目符合国家利益，并将通过科学领域和工业中迫切需要的关键软件培训来支持STEM学科。暗物质社区由1000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20多种不同的实验。以多种方式搜索暗物质导致了完全不同的计算训练，而且常常是不充分的。该项目解决了培训问题，以最大限度地扩大对整个外地的影响。代表三个领先的暗物质实验，项目调查人员将为系统的数据科学教育开发教育材料和培训讲习班，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讲习班，目标是发展一个教员社区，并免费分发和重复使用一套培训材料。除了在罕见事件搜索方面的特定领域培训外，还将在必要时通过与软件和数据木匠等合作伙伴合作来发展基本的计算知识。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她们在该领域的地位的具体目标。此外，该项目还将通过黑客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。该项目由计算机和信息科学与工程局的高级网络基础设施办公室和数学和物理科学局的物理部资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。