发表机构
Princeton University; University of Nebraska–Lincoln（普林斯顿大学; 内布拉斯加大学林肯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究CMS分析流程，通过集成挑战评估软件栈就绪度，涵盖数据处理、机器学习等任务，为HL-LHC时代做准备。
AI 中文摘要
位于欧洲核子研究中心（CERN）的即将到来的高亮度大型强子对撞机（HL-LHC）将为高能物理（HEP）提供前所未有的数据量。这些丰富的信息为科学发现提供了重要机遇，但其规模对传统分析工作流程构成了挑战。在本文中，我们介绍了为满足HL-LHC需求而开发的CMS分析流程。这些流程建立在更广泛的科学Python生态系统之上，并辅以专为高能物理设计的解决方案。这项工作的核心焦点是集成挑战（Integration Challenge），这是由IRIS-HEP牵头的一项工作，旨在评估所开发软件栈在真实物理分析中的就绪程度，并提高分析设施为HL-LHC时代做好准备的水平。集成挑战充当端到端集成测试：通过实现完整的物理分析流程，它评估工具的互操作性以及分析人员的整体用户体验。当前的流程包括列式数据处理、机器学习、统计推断和可视化任务，涵盖各种CMS分析场景。此外，集成挑战还探索了使用不同工具和数据格式提供缩减数据的有效策略，并评估了用于高能物理分析的ServiceX数据交付系统。在整个测试阶段，我们还研究了几个原型服务，例如直方图即服务（histogram-as-a-service）功能，以及其他可能支持未来HL-LHC分析工作流程的新兴服务。
英文摘要
The upcoming High-Luminosity Large Hadron Collider (HL-LHC) at CERN will deliver an unprecedented volume of data for High Energy Physics (HEP). This wealth of information offers significant opportunities for scientific discovery, but its scale challenges traditional analysis workflows. In this paper, we present CMS analysis pipelines being developed to meet HL-LHC demands. These pipelines build on the broader scientific Python ecosystem, complemented by solutions specifically designed for HEP. A central focus of this work is the Integration Challenge, an IRIS-HEP led effort aimed at assessing the readiness of developed software stack to be used in real world physics analysis and improving the readiness of analysis facilities for the HL-LHC era. The Integration Challenge acts as an end-to-end integration test: by implementing a complete physics analysis pipeline, it evaluates tool interoperability and the overall user experience for analysts. The current pipeline includes columnar data processing, machine learning, statistical inference, and visualization tasks covering a variety of CMS analysis scenarios. In addition, the Integration Challenge explores efficient strategies for delivering skimmed data using diverse tools and data formats, as well as evaluating the ServiceX data-delivery system for HEP analyses. Throughout the testing phase, we also investigated several prototype services, such as histogram-as-a-service capabilities, along with other emerging services that may support future HL-LHC analysis workflows.
