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Fast 3D particle reconstruction using a convolutional neural network: application to dusty plasmas
(2021)
AbstractWe present an algorithm to reconstruct the three-dimensional positions of particles in a dense cloud of particles in a dusty plasma using a convolutional neural network. The approach is found to be very fast and yields a relatively high accuracy. In this paper, we describe and examine the approach regarding the particle number and the reconstruction accuracy using synthetic data and experimental data. To show the applicability of the approach the 3D positions of particles in a dense dust cloud in a dusty plasma under weightlessness are reconstructed from stereoscopic camera images using the prescribed neural network.
Three-dimensional (3D) dynamical properties of fast particles being injected into the void region of a dusty plasma under microgravity conditions have been measured. For that purpose, a stereoscopic camera setup of three cameras has been developed that is able to track and reconstruct the 3D trajectories of individual dust particles. From more than 500 particle trajectories, the force field inside the void region and its influence on particle movement are derived and analyzed in 3D. It is shown that the force field is dominated by forces pointing radially out of the void and that this radial character is reflected in the velocity distributions of particles leaving the void. Furthermore, the structure of the force field is used for measuring the neutral gas friction for the particles inside the void.