Determination of Plasma Density and Temperature Gradients through the X-ray Spectroscopy with Deep Learning

Short-pulse, laser-solid interactions provide a unique platform to develop well-characterized laboratory high-energy density (HED) matter conditions to diagnose fundamental properties such as opacity and equations of state. These measurements are needed to benchmark atomic physics models and simulations tools. However, analysis of such plasmas remains challenging due to the rapid temporal and spatial evolution of the emitting plasma. Recent work has shown that the use of deep learning can provide enhanced analysis of hard X-ray spectra relevant to HED plasmas in both speed and complexity. Here, we present the model development of a neural network trained on the collisional-radiative modeling code SCRAM that is capable of extracting the temperature and density profiles, and hot electron fraction. This model is applied to experimental data performed at Colorado State University’s ALEPH laser where high-resolution (E/ΔE > 7500) X-ray spectroscopy of copper K-shell emission was used to generate micron-scale, near solid-density plasmas with electron densities exceeding 1024 cm-3 and temperatures exceeding 3 keV.

Citations:

[1] Mariscal, D. A. et al., Enhanced analysis of experimental x-ray spectra through deep learning. Phys. Plasmas 1 September 2022; 29 (9): 093901. https://doi.org/10.1063/5.0097777