  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.
