Problem
Research questions and friction points this paper is trying to address.
detector design
high-dimensional parameter space
multi-objective optimization
AI-assisted workflow
Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing
🤖 AI Summary
This work addresses the inefficiency and insufficient automation in multi-objective optimization over high-dimensional parameter spaces commonly encountered in high-energy physics detector design. To tackle this challenge, the study presents the first deep integration of multi-objective Bayesian optimization with the PanDA-iDDS distributed workflow system, establishing a general-purpose and scalable intelligent optimization framework. By orchestrating collaborative simulation tasks across heterogeneous computing resources, the framework substantially enhances the automation, computational efficiency, and scalability of the optimization process. Its effectiveness is demonstrated through successful applications in the design optimization of both ePIC and dRICH detectors.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-assisted workflow
multi-objective Bayesian optimization
distributed computing
detector design optimization
PanDA-iDDS
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