Bayesian inference of x-ray diffraction spectra from warm dense matter with the one-component-plasma model

Area of Science:

• Plasma Physics
• Materials Science
• Statistical Mechanics

Background:

• Laser-shocked aluminum experiments provide valuable data on warm dense matter.
• Interpreting x-ray diffraction spectra requires robust theoretical models.

Purpose of the Study:

• To assess the effectiveness of Bayesian inference with the one-component-plasma (OCP) model for analyzing x-ray diffraction spectra.
• To determine the ionic density and temperature of laser-shocked aluminum.

Main Methods:

• Bayesian inference applied to x-ray diffraction spectra.
• Utilizing the one-component-plasma (OCP) model and its static structure factor.
• Employing a recently derived analytic fit for the OCP static structure factor.
• Approximating the atomic form factor with an exponential function.

Main Results:

• The OCP model with Bayesian inference accurately estimates ionic density and temperature.
• Electronic temperature is determined by comparing the approximated form factor with an average atom model.
• Out-of-equilibrium states (electrons hotter than ions) were observed at early time delays.
• Thermal equilibrium was reached at later time delays.

Conclusions:

• Bayesian inference combined with the OCP model is a powerful tool for analyzing warm dense matter.
• This approach is crucial for interpreting x-ray Thomson scattering measurements at large laser facilities.
• The study validates the OCP model's performance in estimating plasma parameters from experimental data.