Automated Bayesian high-throughput estimation of plasma temperature and density from emission spectroscopy

Area of Science:

• Plasma Physics
• Spectroscopy
• Computational Physics

Background:

• Accurate plasma diagnostics are crucial for fusion energy research.
• Traditional methods for estimating plasma parameters can be time-consuming and less reliable.
• Atomic emission spectroscopy provides rich data but requires sophisticated analysis.

Purpose of the Study:

• To develop and validate a novel, automated high-throughput method for plasma parameter estimation.
• To integrate Bayesian inference with physical models for enhanced diagnostic accuracy.
• To apply the methodology to magneto-inertial fusion experiments.

Main Methods:

• Utilized atomic emission spectroscopy for data acquisition.
• Implemented Bayesian inference coupled with physical and measurement models.
• Analyzed experimental data from the Plasma Liner Experiment (PLX) at Los Alamos National Laboratory (LANL).

Main Results:

• Demonstrated enhanced accuracy and reliability in plasma temperature and density estimation.
• Successfully revealed underlying hidden structures within the experimental data.
• Validated the effectiveness of the Bayesian approach in complex plasma environments.

Conclusions:

• The novel approach offers a significant advancement in automated plasma diagnostics.
• This methodology has broad implications for nuclear instrumentation and fusion research.
• Further research can expand its application in various plasma-related scientific domains.