论文标题
脆度原子蒸气中等离子体通道的Schlieren成像的机器学习方法
Machine learning methods for Schlieren imaging of a plasma channel in tenuous atomic vapor
论文作者
论文摘要
我们研究了Schlieren成像设置的用法，以测量原子蒸气中血浆通道的几何尺寸。近谐振探针光用于在脆弱的蒸气中对等离子体通道进行成像，并测试了机器学习技术以从图像中提取定量信息。通过构建一个模拟信号的数据库，该数据库具有一系列用于训练深神经网络的等离子体参数，我们证明它们可以从Schlieren图像中从Schlieren图像中提取，并以高度准确地提取等离子通道的位置，半径和最大电离分数，以及等离子通道核心和联合Vapor的核心区域之间的过渡区域。我们通过有监督的学习来测试几种不同的神经网络架构，并表明网络提供的参数估计相对于测量过程中可能发生的实验参数的轻微变化具有弹性。
We investigate the usage of a Schlieren imaging setup to measure the geometrical dimensions of a plasma channel in atomic vapor. Near resonant probe light is used to image the plasma channel in a tenuous vapor and machine learning techniques are tested for extracting quantitative information from the images. By building a database of simulated signals with a range of plasma parameters for training Deep Neural Networks, we demonstrate that they can extract from the Schlieren images reliably and with high accuracy the location, the radius and the maximum ionization fraction of the plasma channel as well as the width of the transition region between the core of the plasma channel and the unionized vapor. We test several different neural network architectures with supervised learning and show that the parameter estimations supplied by the networks are resilient with respect to slight changes of the experimental parameters that may occur in the course of a measurement.
