HEP统计推断生态系统的现状与展望
Status and Prospects of the HEP Statistical Inference Ecosystem
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中文总结 AI 辅助
本文综述了高能物理统计推断工具面临的挑战，并展望了基于RooFit自动微分、JAX互操作及HS3通信的改进方向，以应对模型复杂性和性能需求。
中文摘要 AI 辅助
统计推断是高能物理（HEP）分析中的关键组成部分。历史上基于RooFit和RooStats，实验所使用的统计工具如今正面临前所未有的挑战，例如统计模型复杂性的迅速增长（涉及数百个感兴趣参数和数千个 nuisance 参数）、大规模似然最小化中对可扩展性能的需求，以及跨日益多样化的工具、计算硬件、框架和库（尤其是机器学习领域内开发和使用的那些）生态系统的互操作性要求。本文总结了部分主要LHC实验（CMS、ATLAS）所用统计工具的现状和未来计划，重点介绍了来自ROOT领域（RooFit自动微分）、与现代库（JAX）的互操作性以及跨框架通信（HS3）方面的改进。
英文摘要
Statistical inference is a crucial part of HEP analyses. Historically based on RooFit and RooStats, the statistical tools used by the experiments are now facing unprecedented challenges, such as the rapidly growing complexity of statistical models (involving hundreds of parameters of interest and thousands of nuisance parameters), the need for scalable performance in large likelihood minimizations, and the demand for interoperability across an increasingly diverse ecosystem of tools, computational hardware, and frameworks and libraries, especially the ones developed and used within the machine learning world. This contribution summarizes status and future plans for the statistical tools used by some of the main LHC experiments (CMS, ATLAS), with a focus on improvements coming from the ROOT world (RooFit automatic differentiation), interoperability with modern libraries (JAX) and communication across frameworks (HS3).
发表机构
- Princeton University（普林斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。
