论文标题
机器学习协助非破坏性横梁轮廓成像
Machine learning assisted non-destructive transverse beam profile imaging
论文作者
论文摘要
我们提出了一种非破坏性梁曲线成像概念，该概念利用机器学习工具，即具有梯度下降样最小化的遗传算法。带电梁周围的电磁场携带有关其横向轮廓的信息。条线型梁位置监视器（本研究中有八个探针）的电极可以拾取该信息以可视化光束轮廓。我们使用遗传算法来改变任意的高斯光束，以使其最终重建原始梁的横向位置和形状。该算法需要一个信号，该信号被带线电极拾取，以及（精确或近似）梁尺寸的知识。它也可以看到相当扭曲的光束的轮廓。
We present a non-destructive beam profile imaging concept that utilizes machine learning tools, namely genetic algorithm with a gradient descent-like minimization. Electromagnetic fields around a charged beam carry information about its transverse profile. The electrodes of a stripline-type beam position monitor (with eight probes in this study) can pick up that information for visualization of the beam profile. We use a genetic algorithm to transform an arbitrary Gaussian beam in such a way that it eventually reconstructs the transverse position and the shape of the original beam. The algorithm requires a signal that is picked up by the stripline electrodes, and a (precise or approximate) knowledge of the beam size. It can visualize the profile of fairly distorted beams as well.
