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Updated: Jun 28, 2025

Non-equilibrium Microwave Plasma for Efficient High Temperature Chemistry
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Bayesian plasma model selection for Thomson scattering.

Jean Luis Suazo Betancourt1, Samuel J Grauer2, Junhwi Bak3

  • 1School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, Georgia 30318, USA.

The Review of Scientific Instruments
|April 17, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces Bayesian inference and model selection for Laser Thomson scattering (LTS) diagnostics in low-temperature plasmas. The method accurately identifies plasma types and validates uncertainty quantification, crucial for space propulsion applications.

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Area of Science:

  • Plasma Physics
  • Diagnostic Techniques
  • Computational Science

Background:

  • Laser Thomson scattering (LTS) measures electron velocity distribution functions.
  • Accurate interpretation requires submodel selection and uncertainty quantification (UQ).
  • Challenges exist for low-density, low-temperature, non-Maxwellian plasmas in space electric propulsion.

Purpose of the Study:

  • Apply Bayesian inference and model selection to LTS diagnostics.
  • Evaluate performance with synthetic and experimental data.
  • Characterize electron velocity distributions and validate UQ methods.

Main Methods:

  • Utilized Bayesian inference and model selection for LTS data analysis.
  • Employed synthetic data to test performance across signal-to-noise ratios and model fidelities.
  • Applied the framework to experimental data from a nanosecond pulsed plasma.

Main Results:

  • Accurately detected plasma types (Maxwellian vs. non-Maxwellian) for signal-to-noise ratios > 5.
  • Validated 95% confidence intervals from least-squares inversion for UQ.
  • Demonstrated divergence in epistemic correlations between least-squares and Bayesian methods with increasing parameters.
  • Obtained electron temperature and density estimates for pulsed plasma experiments.
  • Showed strong support for Maxwellian distribution at 10 kV discharge voltage.

Conclusions:

  • Bayesian inference is essential for accurate correlations in complex plasma systems.
  • The developed framework reliably performs automated model selection and UQ for LTS.
  • Results provide valuable insights into plasma characteristics for space propulsion and pulsed plasma applications.