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Bibliographic Details
Main Authors:	Anderson, Derek, Bashyal, Amit, Diefenthaler, Markus, Fanelli, Cristiano, Guan, Wen, Horn, Tanja, Lin, Alex Jentsch Meifeng, Maeno, Tadashi, Nagai, Kei, Nayak, Hemalata, Pecar, Connor, Suresh, Karthik, Tsai, Fang-Ying, Vossen, Anselm, Wang, Tianle, Wenaus, Torre
Format:	Preprint
Published:	2026
Subjects:	Distributed, Parallel, and Cluster Computing Artificial Intelligence
Online Access:	https://arxiv.org/abs/2603.30014
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Table of Contents:

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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