Extracting forces from noisy dynamics in dusty plasmas
Abstract
Extracting environmental forces from noisy data is a common yet challenging task in complex physical systems. Machine learning (ML) represents a robust approach to this problem, yet is mostly tested on simulated data with known parameters. Here we use supervised ML to extract the electrostatic, dissipative, and stochastic forces acting on micron-sized charged particles levitated in an argon plasma (dusty plasma). By tracking the sub-pixel motion of particles in subsequent images, we successfully estimated these forces from their random motion. The experiments contained important sources of non-Gaussian noise, such as drift and pixel-locking, representing a data mismatch from methods used to analyze simulated data with purely Gaussian noise. Our model was trained on simulated particle trajectories that included all of these artifacts, and used more than 100 dynamical and statistical features, resulting in a prediction with 50\% better accuracy than conventional methods. Lastly, in systems with two interacting particles, the model provided non-contact measurements of the particle charge and Debye length in the plasma environment.
- Publication Date:
- Research Org.:
- Emory Univ., Atlanta, GA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Fusion Energy Sciences (FES)
- OSTI Identifier:
- 1903138
- Alternate Identifier(s):
- OSTI ID: 1886341
- Grant/Contract Number:
- SC0021290
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review. E
- Additional Journal Information:
- Journal Volume: 106; Journal Issue: 3; Journal ID: ISSN 2470-0045
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 70 PLASMA PHYSICS AND FUSION TECHNOLOGY; Stochastic inference; Dusty or complex plasma; Brownian dynamics; Machine learning
Citation Formats
Yu, Wentao, Cho, Jonathan, and Burton, Justin Clifford. Extracting forces from noisy dynamics in dusty plasmas. United States: N. p., 2022.
Web. doi:10.1103/physreve.106.035303.
Yu, Wentao, Cho, Jonathan, & Burton, Justin Clifford. Extracting forces from noisy dynamics in dusty plasmas. United States. https://doi.org/10.1103/physreve.106.035303
Yu, Wentao, Cho, Jonathan, and Burton, Justin Clifford. Fri .
"Extracting forces from noisy dynamics in dusty plasmas". United States. https://doi.org/10.1103/physreve.106.035303. https://www.osti.gov/servlets/purl/1903138.
@article{osti_1903138,
title = {Extracting forces from noisy dynamics in dusty plasmas},
author = {Yu, Wentao and Cho, Jonathan and Burton, Justin Clifford},
abstractNote = {Extracting environmental forces from noisy data is a common yet challenging task in complex physical systems. Machine learning (ML) represents a robust approach to this problem, yet is mostly tested on simulated data with known parameters. Here we use supervised ML to extract the electrostatic, dissipative, and stochastic forces acting on micron-sized charged particles levitated in an argon plasma (dusty plasma). By tracking the sub-pixel motion of particles in subsequent images, we successfully estimated these forces from their random motion. The experiments contained important sources of non-Gaussian noise, such as drift and pixel-locking, representing a data mismatch from methods used to analyze simulated data with purely Gaussian noise. Our model was trained on simulated particle trajectories that included all of these artifacts, and used more than 100 dynamical and statistical features, resulting in a prediction with 50\% better accuracy than conventional methods. Lastly, in systems with two interacting particles, the model provided non-contact measurements of the particle charge and Debye length in the plasma environment.},
doi = {10.1103/physreve.106.035303},
journal = {Physical Review. E},
number = 3,
volume = 106,
place = {United States},
year = {Fri Sep 09 00:00:00 EDT 2022},
month = {Fri Sep 09 00:00:00 EDT 2022}
}
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