Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input parameters is often the first step when conducting accelerator-based experiments. Currently used techniques for characterization, such as grid-like parameter sampling scans, become impractical when extended to higher dimensional input spaces, when complicated measurement constraints are present, or prior information known about the beam response is scarce. Here in this work, we describe an adaptation of the popular Bayesian optimization algorithm, which enables a turn-key exploration of input parameter spaces. Our algorithm replaces the need for parameter scans while minimizing prior information needed about the measurement’s behavior and associated measurement constraints. We experimentally demonstrate that our algorithm autonomously conducts an adaptive, multi-parameter exploration of input parameter space, potentially orders of magnitude faster than conventional grid-like parameter scans, while making highly constrained, single-shot beam phase-space measurements and accounts for costs associated with changing input parameters. In addition to applications in accelerator-based scientific experiments, this algorithm addresses challenges shared by many scientific disciplines, and is thus applicable to autonomously conducting experiments over a broad range of research topics.

粒子加速器是化学、生物和物理科学中宝贵的发现引擎。在进行基于加速器的实验时，表征加速光束对加速器输入参数的响应通常是第一步。当前使用的表征技术（例如网格状参数采样扫描）在扩展到更高维度的输入空间时变得不切实际，当存在复杂的测量约束时，或者关于光束响应的先验信息很少。在这项工作中，我们描述了流行的贝叶斯优化算法的改编，它能够对输入参数空间进行交钥匙探索。我们的算法取代了对参数扫描的需求，同时最小化了有关测量行为和相关测量约束所需的先验信息。我们通过实验证明，我们的算法自动对输入参数空间进行自适应、多参数探索，可能比传统的类似网格的参数扫描快几个数量级，同时进行高度受限的单次光束相空间测量并考虑成本与更改输入参数相关。除了在基于加速器的科学实验中的应用外，该算法还解决了许多科学学科共同面临的挑战，因此适用于在广泛的研究主题上自主进行实验。同时进行高度受限的单次光束相空间测量，并考虑与更改输入参数相关的成本。除了在基于加速器的科学实验中的应用外，该算法还解决了许多科学学科共同面临的挑战，因此适用于在广泛的研究主题上自主进行实验。同时进行高度受限的单次光束相空间测量，并考虑与更改输入参数相关的成本。除了在基于加速器的科学实验中的应用外，该算法还解决了许多科学学科共同面临的挑战，因此适用于在广泛的研究主题上自主进行实验。