Machine learning methods for Schlieren imaging of a plasma channel in tenuous atomic vapor
This work provides a method for precise plasma diagnostics in atomic vapor, which is incremental as it applies existing machine learning techniques to a specific experimental setup.
The paper tackled the problem of measuring plasma channel dimensions in atomic vapor using Schlieren imaging, and demonstrated that deep neural networks can reliably extract location, radius, maximum ionization fraction, and transition region width from images with high accuracy.
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.