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Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference.
- Source :
-
The Review of scientific instruments [Rev Sci Instrum] 2024 Aug 01; Vol. 95 (8). - Publication Year :
- 2024
-
Abstract
- Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. We present a spectral decomposition method that allows for corrections to line positions and bound-bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.<br /> (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)
Details
- Language :
- English
- ISSN :
- 1089-7623
- Volume :
- 95
- Issue :
- 8
- Database :
- MEDLINE
- Journal :
- The Review of scientific instruments
- Publication Type :
- Academic Journal
- Accession number :
- 39171981
- Full Text :
- https://doi.org/10.1063/5.0219464