Speaker
Description
Artificial Intelligence (AI) is poised to play a central role in the design and optimization of complex, large-scale detectors, such as the future ePIC experiment at the Electron-Ion Collider (EIC), an international next-generation QCD facility in the United States.
The ePIC experiment consists in an integrated detector comprising a central apparatus complemented by forward and backward subsystems, designed to support a broad physics program while meeting stringent performance requirements within cost, mechanical, and geometric constraints. Addressing these competing demands requires scalable and reproducible optimization strategies operating over a multidimensional, multi-objective design space. This contribution presents recent developments of AID$^2$E, a scalable and distributed AI-assisted detector design and optimization environment motivated by the future EIC program but broadly applicable beyond it. While developed in an experiment-agnostic manner, AID$^2$E has been deployed using the official ePIC software stack and its Geant4-based simulations, combining transparent detector parameterization with modern multi-objective optimization techniques to enable systematic exploration of high-dimensional design spaces. The workflow employs the PanDA and iDDS workload-management systems$-$successfully deployed in experiments such as ATLAS and the Rubin Observatory$-$to orchestrate large-scale simulation and optimization campaigns.
Recent enhancements include expanded PanDA-based workflow support, extended compatibility with PanDA, Slurm and local execution modes, and a Function-as-a-Task (FaaT) system that translates detector-design and optimization workflows into large-scale distributed processing pipelines across heterogeneous computing environments.
Ongoing work focuses on advanced optimization strategies, improved data-analysis tools for navigating design trade-offs, and the planned integration of large language models to enable enhanced workflow orchestration and control with human-in-the-loop oversight.
These advances position AID$^2$E as an extensible platform for large-scale detector design and optimization, with applications transferable to future nuclear and particle physics experiments and to detector-optimization tasks in ongoing Jefferson Lab experiments, where AID$^2$E has been successfully integrated for calibration and alignment.
AID$^2$E thus exemplifies the transformative role of AI in automating and scaling complex scientific workflows.