AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case
This work addresses the challenge of computationally intensive detector R&D for nuclear physics researchers, though it appears incremental as it applies existing optimization methods to a specific case.
The authors tackled the problem of optimizing detector design for particle physics experiments by proposing a Bayesian optimization and machine learning approach, demonstrating that their automated framework outperforms the baseline design for a dual-radiator RICH detector at the Electron-Ion Collider.
Advanced detector R&D requires performing computationally intensive and detailed simulations as part of the detector-design optimization process. We propose a general approach to this process based on Bayesian optimization and machine learning that encodes detector requirements. As a case study, we focus on the design of the dual-radiator Ring Imaging Cherenkov (dRICH) detector under development as part of the particle-identification system at the future Electron-Ion Collider (EIC). The EIC is a US-led frontier accelerator project for nuclear physics, which has been proposed to further explore the structure and interactions of nuclear matter at the scale of sea quarks and gluons. We show that the detector design obtained with our automated and highly parallelized framework outperforms the baseline dRICH design within the assumptions of the current model. Our approach can be applied to any detector R&D, provided that realistic simulations are available.