Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference.

Area of Science:

• Plasma Physics
• Computational Astrophysics
• Nuclear Fusion

Background:

• Bayesian inference is crucial for uncertainty quantification in x-ray spectroscopy.
• Detailed plasma simulations for spectral analysis are computationally intensive.
• Discrepancies in atomic data hinder direct comparison between simulations and experimental spectra.

Purpose of the Study:

• To develop a faster method for analyzing x-ray spectroscopy data.
• To enable rigorous testing of theoretical plasma models.
• To improve the accuracy of plasma parameter extraction from experimental data.

Main Methods:

• Implemented a spectral decomposition method for data fitting.
• Utilized a neural network (NN) surrogate model to accelerate spectral calculations.
• Trained the NN on data from isobaric hot-spot models using the Cretin code.

Main Results:

• The NN surrogate model significantly speeds up spectral emission calculations.
• The method allows for corrections to line positions and opacities.
• Enables detailed statistical analysis of parameterized plasma models.

Conclusions:

• The NN-accelerated spectral analysis facilitates quantitative feedback for theoretical models.
• This approach improves the reliability of comparing simulated and measured x-ray spectra.
• Guides future experiments and enhances understanding of plasma conditions.