arXiv 2603.300142026-06-26cs.DCcs.AI

基于分布式计算的可扩展AI辅助探测器设计优化工作流管理

Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing

Brookhaven National Laboratory（布鲁克海文国家实验室）；The Catholic University of America（美国天主教大学）；Duke University（杜克大学）；Stony Brook University（石溪大学）

Derek Anderson, Amit Bashyal, Markus Diefenthaler, Cristiano Fanelli, Wen Guan, Tanja Horn, Alex Jentsch Meifeng Lin, Tadashi Maeno, Kei Nagai, Hemalata Nayak, … 展开作者

Derek Anderson, Amit Bashyal, Markus Diefenthaler, Cristiano Fanelli, Wen Guan, Tanja Horn, Alex Jentsch Meifeng Lin, Tadashi Maeno, Kei Nagai, Hemalata Nayak, Connor Pecar, Karthik Suresh, Fang-Ying Tsai, Anselm Vossen, Tianle Wang, Torre Wenaus

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AI总结：

提出一种结合多目标贝叶斯优化与PanDA-iDDS工作流引擎的AI辅助框架，用于协调异构资源上的迭代仿真，实现高维参数空间的高效探索，并在ePIC和dRICH探测器设计中验证了自动化、可扩展性和效率提升。

摘要：

生产与分布式分析（PanDA）系统最初为CERN大型强子对撞机（LHC）上的ATLAS实验开发，现已发展成为一个在分布式计算资源上编排大规模工作流的稳健平台。结合其智能分布式调度与分发（iDDS）组件，PanDA通过可扩展且灵活的工作流引擎支持AI/ML驱动的工作流。我们提出了一个用于探测器设计优化的AI辅助框架，该框架将多目标贝叶斯优化与PanDA-iDDS工作流引擎集成，以协调跨异构资源的迭代仿真。该框架解决了现代探测器设计中固有的高维参数空间探索挑战。我们使用基准问题以及电子-离子对撞机（EIC）的ePIC和dRICH探测器的实际研究来演示该框架。结果表明，在多目标优化中实现了改进的自动化、可扩展性和效率。这项工作为AI驱动的探测器设计和其他计算密集型科学应用建立了一个灵活且可扩展的范式。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。