Microparticle cloud imaging and tracking for data-driven plasma science

Wang, Zhehui; Xu, Jiayi; Kovach, Yao E.; Wolfe, Bradley T.; Thomas, Edward; Guo, Hanqi; Foster, John E.; Shen, Han-Wei

doi:10.1063/1.5134787

Title: Microparticle cloud imaging and tracking for data-driven plasma science

Journal Article · 05 March 2020 · Physics of Plasmas

DOI: https://doi.org/10.1063/1.5134787 · OSTI ID:1659207

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^[1]; Thomas, Edward ^[4];

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^[3]; Shen, Han-Wei ^[2]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
The Ohio State Univ., Columbus, OH (United States)
Univ. of Michigan, Ann Arbor, MI (United States)
Auburn Univ., AL (United States)
Argonne National Lab. (ANL), Lemont, IL (United States)

Oceans of image and particle track data encountered in plasma interactions with microparticle clouds motivate development and applications of machine-learning (ML) algorithms. A local-constant-velocity tracker, a Kohonen neural network or self-organizing map, the feature tracking kit, and U-Net are described and compared with each other for microparticle cloud datasets generated from exploding wires, dusty plasmas, and atmospheric plasmas. Particle density and the signal-to-noise ratio have been identified as two important factors that affect the tracking accuracy. Fast Fourier transform is used to reveal how U-Net, a deep convolutional neural network developed for non-plasma applications, achieves the improvements for noisy scenes. Viscous effects are revealed in the ballistic motions of the particles from the exploding wires and atmospheric plasmas. Subdiffusion of microparticles satisfying Δr²∝t^k (k=0.84±0.02) is obtained from the dusty plasma datasets. Microparticle cloud imaging and tracking, when enhanced with data and ML models, present new possibilities for plasma physics.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Fusion Energy Sciences (FES)

Grant/Contract Number:: 89233218CNA000001; AC02-06CH11357; SC0018058

OSTI ID:: 1659207

Alternate ID(s):: OSTI ID: 1603191; OSTI ID: 1774503

Report Number(s):: LA-UR-19-30795; TRN: US2203406

Journal Information:: Physics of Plasmas, Vol. 27, Issue 3; ISSN 1070-664X

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 8 works

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70 PLASMA PHYSICS AND FUSION TECHNOLOGY
magnetic fusion energy
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Title: Microparticle cloud imaging and tracking for data-driven plasma science

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