Microparticle cloud imaging and tracking for data-driven plasma science

• Published in: arXiv.org 2019-11
• Main Authors: Wang, Zhehui, Xu, Jiayi, Kovach, Yao E, Wolfe, Bradley T, Thomas, Edward, Guo, Hanqi, Foster, John E, Han-Wei, Shen
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Atmospheric models; Cameras; Clouds; Computer simulation; Datasets; Dusty plasmas; Exploding wires; Fast Fourier transformations; Frames per second; Imaging; Microparticles; Neural networks; Particle density (concentration); Particle tracking; Physics; Plasma; Plasma physics; Polynomials; Self organizing maps; Signal to noise ratio
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1911.01000

Large data sets give rise to the `fourth paradigm' of scientific discovery and technology development, extending other approaches based on human intuition, fundamental laws of physics, statistics and intense computation. Both experimental and simulation data are growing explosively in plasma science and technology, motivating data-driven discoveries and inventions, which are currently in infancy. Here we describe recent progress in microparticle cloud imaging and tracking (mCIT, \(\mu\)CIT) for laboratory plasma experiments. Three types of microparticle clouds are described: from exploding wires, in dusty plasmas and in atmospheric plasmas. The experimental data sets are obtained with one or more imaging cameras at a rate up to 100k frames per second (fps). A physics-constrained motion tracker, a Kohonen neural network (KNN) or self-organizing map (SOM), the feature tracking kit (FTK), and U-Net are described and compared with each other for particle tracking using the datasets. Particle density and signal-to-noise ratio have been identified as two important factors that affect the tracking accuracy. Fast Fourier transform (FFT) is used to reveal how U-Net, a deep convolutional neural network (CNN) developed for non-plasma applications, achieves the improvements for noisy scenes. The fitting parameters for a simple polynomial track model are found to group into clusters that reveal the geometry information about the camera setup. The mCIT or \(\mu\)CIT techniques, when enhanced with data models, can be used to study the microparticle- or Debye-length scale plasma physics. The datasets are also available for ML code development and comparisons of algorithms.