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arXiv · 2603.30014

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

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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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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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. 2026-03-31. Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing. https://arxiv.org/abs/2603.30014

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