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. Our approach can be applied to any detector R&D, provided that realistic simulations are available.

中文翻译：

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面向未来电子离子对撞机的 AI 优化探测器设计：双散热器 RICH 案例

作为探测器设计优化过程的一部分，高级探测器研发需要执行计算密集型和详细的模拟。我们提出了一种基于贝叶斯优化和机器学习的通用方法来编码检测器要求。作为案例研究，我们专注于正在开发的双辐射器环形成像切伦科夫 (dRICH) 探测器的设计，作为未来电子离子对撞机 (EIC) 的粒子识别系统的一部分。EIC 是美国主导的核物理前沿加速器项目，旨在进一步探索海夸克和胶子尺度上核物质的结构和相互作用。我们表明，使用我们的自动化和高度并行化框架获得的检测器设计优于基线 dRICH 设计。