Search

Abstract: Intense laser light traversing a thin layer of nematic liquid crystal is an example of a simple, easy to realise physical system, that shows very complex behaviour. Light can turn the optical axis of this uniaxial medium while propagating through it, and the dynamical behaviour that results ranges from simple orientational instabilities, through low-dimensional chaotic behaviour, to spatiotemporal pattern formation. In this paper, we review recent advances in the theoretical description of the complex phenomena that light can induce in nematic liquid crystals. We discuss the various approximations made in the models, their range of applicability, and contrast their results with experimental observations. In particular, we discuss the achievements of the plane wave approximation in various geometries, and examine how the numerous bifurcation scenarios calculated from the models can be used to interpret observations. We treat with special emphasis the results achieved in the description of the strongly nonlinear regime, where experiments have revealed interesting bifurcations and chaotic behaviour. We also discuss the mostly open problems of transverse pattern formation and the effects that can be attributed to the finite cross-section of the laser beam. [Copyright &y& Elsevier]

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 the tenuous vapor and machine learning techniques are tested for extracting quantitative information from the images. We build a database of simulated signals with a range of plasma parameters for training Deep Neural Networks, and demonstrate that they can estimate, 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. • Machine learning programs can evaluate images provided by a laser probe. • The parameters of a plasma channel can be estimated quickly and accurately. • Parameter estimation works well for measurements taken under varying circumstances. [ABSTRACT FROM AUTHOR]