用AI优化探测器设计，大幅提升计算效率与自动化水平。
Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing
- 结合贝叶斯优化与分布式调度系统，实现多目标参数搜索。
- 在ePIC和dRICH探测器上验证，显著提升高维空间探索效率。
- 适合需要大规模仿真的粒子物理与科学计算研究者使用。
原本为欧洲核子研究中心大型强子对撞机ATLAS实验开发的生产与分布式分析（PanDA）系统，已演变为跨分布式计算资源协调大规模工作流的稳健平台。其智能分布式调度（iDDS）组件支持基于AI/ML的工作流，通过可扩展、灵活的工作流引擎实现高效调度。本文提出一种用于探测器设计优化的AI辅助框架，将多目标贝叶斯优化与PanDA-iDDS工作流引擎相结合，协调异构资源上的迭代仿真。该框架解决了现代探测器设计中高维参数空间探索的挑战。我们通过基准问题及ePIC与dRICH探测器在电子-离子对撞机（EIC）中的实际研究进行了验证，结果表明自动化程度、可扩展性与效率均显著提升。本工作建立了一种灵活可扩展的AI驱动探测器设计范式，适用于其他计算密集型科学应用。
原文摘要 · Abstract (English)
The Production and Distributed Analysis (PanDA) system, originally developed for the ATLAS experiment at the CERN Large Hadron Collider (LHC), has evolved into a robust platform for orchestrating large-scale workflows across distributed computing resources. Coupled with its intelligent Distributed Dispatch and Scheduling (iDDS) component, PanDA supports AI/ML-driven workflows through a scalable and flexible workflow engine. We present an AI-assisted framework for detector design optimization that integrates multi-objective Bayesian optimization with the PanDA--iDDS workflow engine to coordinate iterative simulations across heterogeneous resources. The framework addresses the challenge of exploring high-dimensional parameter spaces inherent in modern detector design. We demonstrate the framework using benchmark problems and realistic studies of the ePIC and dRICH detectors for the Electron-Ion Collider (EIC). Results show improved automation, scalability, and efficiency in multi-objective optimization. This work establishes a flexible and extensible paradigm for AI-driven detector design and other computationally intensive scientific applications.
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