AI-Assisted Detector Design for the EIC (AID(2)E)

TL;DR

This paper presents a scalable, AI-assisted, multiobjective optimization framework for designing complex detectors at the Electron Ion Collider, integrating advanced AI, simulation, and distributed computing tools to improve design efficiency and performance.

Contribution

It introduces a novel AI-assisted, multiobjective optimization workflow for EIC detector design, leveraging state-of-the-art simulation, parameterization, and distributed computing systems.

Findings

Developed a scalable AI-assisted detector design workflow.

Integrated Geant4 simulations with AI optimization techniques.

Enhanced PanDA system for improved usability and automation.

Abstract

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like the ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using Geant4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.