A: 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 a potential component 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.

We have prepared high-quality, single crystals of SmB 6 under various conditions to improve sample quality. We have measured the resistivity and magnetic susceptibility from room to liquid-helium temperatures to sort samples. We have applied pulsed magnetic fields as high as 50 T at temperatures as low as 40 mK while measuring resistivity. Our samples are of higher quality than previously known. All solvent-grown, single-crystal samples should be etched to remove a surface conductivity.