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

• URL: http://arxiv.org/abs/2603.30014v1
• Date: Tue, 31 Mar 2026 17:11:16 GMT
• Title: Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing
• Abstract summary: We present an AI-assisted framework for detector design optimization that integrates multi-objective Bayesian optimization.<n>We demonstrate the framework using benchmark problems and realistic studies of the ePIC and dRICH detectors for the Electron-Ion Collider (EIC)<n>Results show improved automation, scalability, and efficiency in multi-objective optimization.
• Score: 0.7287445515583322
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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