RESUMO
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.
RESUMO
Deuterium gas puff Z pinches have been used for generation of strong neutron fluxes on the MA class pulse power machines. Due to the low electron density of deuterium Z-pinch plasma, regular laser diagnostics in the visible range cannot be used for observation and study of the pinch. Laser probing at the wavelength of 1064 nm was used for visualization of deuterium plasma. Infrared schlieren and interferometry diagnostics showed the deuterium gas puff plasma dynamics, instabilities, and allowed for the reconstruction of the profile of the plasma density.