Constraining physical models at gigabar pressures.

Area of Science:

• Physics
• Astrophysics
• Materials Science

Background:

• High-energy-density (HED) experiments in convergent geometries achieve extreme pressures (hundreds of millions of atmospheres).
• Measurements from these experiments are highly integrated, necessitating advanced analysis for quantitative data extraction.

Purpose of the Study:

• To develop and demonstrate a general methodology for constraining physics in convergent HED experiments.
• To adapt established physics analysis techniques for HED experimental data.

Main Methods:

• Bayesian inference applied to reconstruct mechanical model parameters of an imploding shell.
• Utilizing data from a thin-shelled direct-drive exploding-pusher experiment on the OMEGA laser system.
• Markov chain Monte Carlo sampling for posterior distribution generation of model parameters.

Main Results:

• Successful reconstruction of shell dynamics and energy transfer during implosion.
• Validation of the methodology using synthetic data from a 1D hydrodynamics code.
• Demonstration of a generalizable approach for HED experiment analysis.

Conclusions:

• The proposed Bayesian inference methodology effectively constrains physics in convergent HED experiments.
• This approach provides a powerful tool for extracting quantitative information from complex HED data.
• The methodology is adaptable for a broad range of HED experimental setups.