分析大挑战中的近期基准测试及与Combine（和HS3）的集成
Recent benchmarks in the Analysis Grand Challenge and integration with Combine (and HS3)
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中文总结 AI 辅助
本文以分析大挑战为例，介绍Combine工具集成到PyHEP生态系统的近期工作，包括性能基准测试、在coffea-casa上的运行示例及HS3集成计划，以提升高能物理分析效率与互操作性。
中文摘要 AI 辅助
分析大挑战（AGC）展示了高能物理（HEP）分析的一个示例。其参考实现使用现代Python包来实现从数据访问到统计模型构建和拟合的主要步骤。用于数据处理和处理的包（coffea、uproot、awkward-array）最近经历了一系列性能优化。虽然不属于HEP Python（PyHEP）生态系统，但Combine工具是CMS分析的支柱，覆盖了过去几年发表的分析的90%以上。因此，有必要以AGC为例，将Combine集成到PyHEP生态系统中。该项目还长期包括为高能物理统计序列化标准（HS3）提供支持和集成，以此作为一种与语言无关的表示似然函数的方式，并能够互换使用不同的框架。在这些会议论文中，我们涵盖了近期在AGC和Combine上开展的部分工作，包括：性能基准测试，涵盖数据处理包近期改进带来的优势；Combine如何集成并在专用基础设施（coffea-casa）中运行的示例；以及将HS3集成到Combine中的示例和计划。
英文摘要
The Analysis Grand Challenge (AGC) showcases an example of HEP analysis. Its reference implementation uses modern Python packages to realize the main steps, from data access to statistical model building and fitting. The packages used for data handling and processing (coffea, uproot, awkward-array) have recently undergone a series of performance optimizations. While not being part of the HEP Python (PyHEP) ecosystem, the Combine tool is a pillar of CMS analyses, covering more than 90% of the analyses published in the last few years. As such, it is necessary to have Combine integrated in the PyHEP ecosystem, using the AGC as example. This project also includes, in the long-term, providing support and integration for the High Energy Physics Statistics Serialization Standard (HS3), as a way to have a language-independent way of representing the likelihood and use different frameworks interchangeably. In these proceedings we cover part of the recent work performed on the AGC and Combine, including: performance benchmarks, covering benefits introduced by the recent improvements in the data processing packages; examples of how Combine can be integrated and run in a dedicated infrastructure (coffea-casa); and examples and plans to integrate HS3 in Combine.
