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

TL;DR

This paper introduces an AI-assisted, scalable workflow framework integrating Bayesian optimization with distributed computing to enhance detector design processes, demonstrated on EIC detector studies.

Contribution

It presents a novel integration of multi-objective Bayesian optimization with the PanDA-iDDS workflow engine for efficient detector design optimization.

Findings

Improved automation and scalability in detector design optimization.

Effective exploration of high-dimensional parameter spaces.

Demonstrated success on realistic EIC detector studies.

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.