用于自主光束线对准的贝叶斯优化中低保真度数据的动态排除
Dynamic Exclusion of Low-Fidelity Data in Bayesian Optimization for Autonomous Beamline Alignment
AI总结：
针对同步加速器光束线对准的贝叶斯优化易受低质量数据干扰的问题，研究提出两种动态排除低保真度数据的方法，均实现有效分类，可提升光束质量并加快优化收敛，助力BNL能源研究使命。
摘要：
同步加速器光源的光束线对准是一个高维、采样成本高昂的优化问题，因为光束需通过一系列动态光学元件进行聚焦。贝叶斯优化（Bayesian Optimization）是一种高效的机器学习方法，可用于寻找光束质量的全局最优值，但该模型很容易因光束偏离传感器边缘或背景噪声导致的故障数据点而性能受损。本研究在布鲁克海文国家实验室（BNL）的国家同步辐射光源II（NSLS-II）装置开展，旨在研究识别不可靠光束质量读数、并阻止优化模型搜寻可能产生低保真度光束的点的方法。研究探索的方法包括：利用尺寸和位置模型的损失分析进行动态剪枝，以及一种基于长度尺度（lengthscale）的遗传算法，用于确定将哪些点纳入模型以实现最优拟合。每种方法都成功实现了高、低保真度点的分类。本研究通过为所有光束线的科研人员提供更高质量的光束，并让其实验更快收敛至这些最优值，推进了BNL应对国家能源挑战的使命。
英文摘要：
Aligning beamlines at synchrotron light sources is a high-dimensional, expensive-to-sample optimization problem, as beams are focused using a series of dynamic optical components. Bayesian Optimization is an efficient machine learning approach to finding global optima of beam quality, but the model can easily be impaired by faulty data points caused by the beam going off the edge of the sensor or by background noise. This study, conducted at the National Synchrotron Light Source II (NSLS-II) facility at Brookhaven National Laboratory (BNL), is an investigation of methods to identify untrustworthy readings of beam quality and discourage the optimization model from seeking out points likely to yield low-fidelity beams. The approaches explored include dynamic pruning using loss analysis of size and position models and a lengthscale-based genetic algorithm to determine which points to include in the model for optimal fit. Each method successfully classified high and low fidelity points. This research advances BNL's mission to tackle our nation's energy challenges by providing scientists at all beamlines with access to higher quality beams, and faster convergence to these optima for their experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
