Collaborative Research: Elements: Shared Data-Delivery Infrastructure to Enable Discovery with Next Generation Dark Matter and Computational Astrophysics Experiments

协作研究：要素：共享数据传输基础设施，以实现下一代暗物质和计算天体物理实验的发现

• 批准号: 2103778
• 负责人: Matthew Turk
• 金额: $ 26.4万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103778&HistoricalAwards=false
• 关键词: Collaborative; Research; Elements; Shared; Data

Modern laboratories provide unprecedented sensitivity to the many different galactic-messengers that stream through our planet by the minute: cosmic rays, light from distant galaxies, elusive neutrinos, and possibly dark matter. Combining this information with models and data from simulations provides insight into how our universe began and continues to evolve -- the scales at which objects first collapsed, the development of stars and galaxies, and the dynamics within our own galaxy.However, this data is often inaccessible: scientists within an experiment or community struggle with the complex, custom-built programs they use to access the data. And switching to a standard format is usually not an option: these data formats are designed for requirements that often do not include cross-experiment synthesis or linking.Junior scientists - let alone the public - can struggle to generate new insights from the data because the data is difficult to access, understand and analyze. The cross-cutting inquiry that could arise from clever reuse and combination of data from different experiments and simulations is rarely conducted.This project makes data accessible both within and across collaborations, providing the infrastructure to search for signals in detectors across the globe. Extending existing efforts to improve data access makes this project possible: yt is software that provides uniform access to simulation data; Kaitai is a data-description language that enables easy access to any data format; Rucio and other tools provide a standard interface that allows data downloads; and ServiceX can identify, subset and process data with little effort from the end user.Scientists have built experiments that offer an incredible wealth of information about our world. This project works to make that information accessible to everyone.Technical DescriptionThe Personal Data-Delivery infrastructure (PONDD) addresses the data challenges of existing dark matter and astrophysics experiments while requiring no changes to existing data formats. This non-invasive, no-changes-necessary support for any file format provides opportunities to expand beyond our two identified use cases, dark matter searches and astrophysics simulations, into many other data-driven science domains that rely on custom file formats.This work delivers an infrastructure that seamlessly delivers data in a well-supported format (such as Parquet) from multiple sources. To successfully deliver cross-experiment data to end users, we bring together ongoing projects from High Energy Physics and the broader NSF community; while this project will involve development of software products (yt and Kaitai) it will also include synthesis of existing investments in cyberinfrastructure and efforts to improve their long-term sustainability.This project is supported by the Office of Advanced Infrastructure 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.

现代实验室提供了前所未有的灵敏度来探测每分钟流过我们星球的许多不同的星系信使：宇宙射线，来自遥远星系的光，难以捉摸的中微子，可能还有暗物质。将这些信息与模拟的模型和数据相结合，可以深入了解我们的宇宙是如何开始和继续演化的——物体首次坍缩的尺度、恒星和星系的发展，以及我们银河系内部的动力学。然而，这些数据通常是无法访问的：实验或社区中的科学家们努力使用复杂的、定制的程序来访问数据。切换到标准格式通常不是一个选择：这些数据格式是为通常不包括交叉实验合成或链接的需求而设计的。初级科学家——更不用说公众了——可能很难从数据中产生新的见解，因为这些数据难以获取、理解和分析。通过巧妙地重用和组合来自不同实验和模拟的数据而产生的交叉调查很少进行。该项目使数据可以在合作内部和跨合作中访问，提供了在全球范围内搜索探测器信号的基础设施。扩大现有的改进数据访问的努力使这个项目成为可能：它是提供统一访问模拟数据的软件；Kaitai是一种数据描述语言，可以轻松访问任何数据格式；Rucio和其他工具提供了允许数据下载的标准接口；ServiceX可以识别、子集和处理数据，而最终用户只需付出很少的努力。科学家们建立的实验为我们的世界提供了令人难以置信的丰富信息。这个项目的目的是让每个人都能获得这些信息。技术描述个人数据交付基础设施（PONDD）解决了现有暗物质和天体物理学实验的数据挑战，同时不需要改变现有的数据格式。这种对任何文件格式的非侵入性、无需更改的支持提供了机会，可以将我们确定的两个用例（暗物质搜索和天体物理学模拟）扩展到许多依赖自定义文件格式的其他数据驱动的科学领域。这项工作提供了一个基础架构，可以无缝地从多个来源以良好支持的格式（例如Parquet）交付数据。为了成功地向最终用户提供交叉实验数据，我们汇集了来自高能物理和更广泛的NSF社区的正在进行的项目；虽然该项目将涉及软件产品（yt和Kaitai）的开发，但它还将包括对网络基础设施的现有投资的综合，并努力提高其长期可持续性。该项目得到了计算机和信息科学与工程理事会先进基础设施办公室以及数学和物理科学理事会物理学部的支持。该奖项反映了美国国家科学基金会的法定使命，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。