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Particle transport constraints via Bayesian spectral fitting of multiple atomic lines
F Sciortino1, N M Cao2, N T Howard1
1Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
The Review of Scientific Instruments
|July 10, 2021
Summary
Bayesian inference methods improve understanding of plasma transport in fusion devices. Analyzing more spectral lines from helium-like ions enhances impurity transport inferences in tokamak plasmas.
Area of Science:
- Nuclear Fusion Science
- Plasma Physics
- Atomic and Molecular Physics
Background:
- Optimized operation of fusion devices requires a deep understanding of plasma transport.
- Advances in measurement and data analysis techniques are crucial for plasma physics research.
- High-resolution spectral analysis offers insights into plasma conditions.
Purpose of the Study:
- To apply Bayesian inference methods for determining experimental particle transport in tokamak plasmas.
- To leverage high-resolution He-like ion spectra for improved plasma transport analysis.
- To investigate the effectiveness of including a larger number of spectral lines for impurity transport inferences.
Main Methods:
- Utilized Bayesian inference methods for particle transport determination.
- Analyzed spectral lines (resonance, forbidden, intercombination, satellite) of He-like Ca.
- Employed the AURORA package for synthetic diagnostics and data analysis.
Main Results:
- Demonstrated the power of Bayesian spectral fitting for transport constraints.
- Showcased how different spectral lines provide varied dependencies on plasma parameters.
- Confirmed improved impurity transport inferences with increased spectroscopic data.
Conclusions:
- Bayesian inference is a powerful tool for analyzing plasma transport.
- High-resolution spectroscopy of He-like ions provides significant constraints on transport models.
- Expanding spectral line analysis enhances the accuracy of impurity transport inferences in fusion research.
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