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

现代实验室对每分钟流经我们星球的许多不同的银河信使提供了前所未有的灵敏度：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。将这些信息与模型和模拟数据相结合，可以洞察我们的宇宙是如何开始并继续演化的--物体最初坍塌的规模，恒星和星系的发展，以及我们银河系内的动力学。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正在为访问数据而使用复杂的定制程序而苦苦挣扎。切换到标准格式通常不是一种选择：这些数据格式是为通常不包括交叉实验综合或链接的要求而设计的。初级科学家--更不用说公众--可能很难从数据中产生新的见解，因为数据很难访问、理解和分析。来自不同实验和模拟的数据的巧妙重用和组合可能产生的交叉查询很少进行。该项目使数据既可以在合作中访问，也可以跨合作访问，为在全球各地的探测器中搜索信号提供了基础设施。扩展现有的改进数据访问的努力使这一项目成为可能：YT是提供对模拟数据的统一访问的软件；Kaitai是一种数据描述语言，可以轻松地访问任何数据格式；Rucio和其他工具提供允许数据下载的标准接口；ServiceX可以识别、子集和处理数据，而最终用户几乎不费力气。科学家们已经建立了实验，提供了关于我们世界的令人难以置信的丰富信息。该项目致力于使每个人都能访问这些信息。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实验的数据挑战，而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，可以扩展到我们的两个已确定的用例-暗物质搜索和天体物理模拟-到许多其他依赖于定制文件格式的数据驱动的科学领域。这项工作提供了一个基础设施，可以无缝地从多个来源以支持良好的格式(如Parquet)提供数据。为了成功地将交叉实验数据提供给最终用户，我们汇集了高能物理和更广泛的NSF社区正在进行的项目；虽然这个项目将涉及软件产品的开发(yt和Kaitai)，但它也将包括对网络基础设施的现有投资的综合，以及提高其长期可持续性的努力。该项目得到了计算机和信息科学与工程局的高级基础设施办公室和数学和物理科学局的物理司的支持。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。

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.