Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing
This work addresses the problem of computationally intensive detector design optimization for scientific applications like the Electron-Ion Collider, representing an incremental advancement by applying existing AI methods to a new domain-specific workflow.
The paper tackles the challenge of exploring high-dimensional parameter spaces in detector design by integrating multi-objective Bayesian optimization with the PanDA-iDDS workflow engine, resulting in improved automation, scalability, and efficiency in multi-objective optimization for detectors like ePIC and dRICH.
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.