High-resolution x-ray spectrometer for x-ray absorption fine structure spectroscopy.

Two extended x-ray absorption fine structure flat crystal x-ray spectrometers (EFX's) were designed and built for high-resolution x-ray spectroscopy over a large energy range with flexible, on-shot energy dispersion calibration capabilities. The EFX uses a flat silicon [111] crystal in the reflection geometry as the energy dispersive optic covering the energy range of 6.3-11.4 keV and achieving a spectral resolution of 4.5 eV with a source size of 50 µm at 7.2 keV. A shot-to-shot configurable calibration filter pack and Bayesian inference routine were used to constrain the energy dispersion relation to within ±3 eV. The EFX was primarily designed for x-ray absorption fine structure (XAFS) spectroscopy and provides significant improvement to the Laboratory for Laser Energetics' OMEGA-60 XAFS experimental platform. The EFX is capable of performing extended XAFS measurements of multiple absorption edges simultaneously on metal alloys and x-ray absorption near-edge spectroscopy to measure the electron structure of compressed 3d transition metals.

The data-driven future of high-energy-density physics.

High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics-however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.

High-resolution x-ray radiography with Fresnel zone plates on the University of Rochester's OMEGA Laser Systems.

Experiments performed at the Laboratory for Laser Energetics with a continuous-wave (cw) x-ray source and on the OMEGA and OMEGA EP Laser Systems [Boehly et al., Opt. Commun. 133, 495 (1997) and Waxer et al., Opt. Photonics News 16, 30 (2005)] have utilized a Fresnel zone plate (FZP) to obtain x-ray images with a spatial resolution as small as ∼1.5 µm. Such FZP images were obtained with a charge-coupled device or a framing camera at energies ranging from 4.5 keV to 6.7 keV using x-ray line emission from both the cw source and high-intensity, laser-beam-illuminated metal foils. In all cases, the resolution test results are determined from patterns and grids backlit by these sources. The resolutions obtained are shown to be due to a combination of the spectral content of the x-ray sources and detector resolution limited by the magnification of the images (14× to 22×). High-speed framing cameras were used to obtain FZP images with frame times as short as ∼30 ps. Double-shell implosions on OMEGA were backlit by laser-irradiated Fe foils, thus obtaining a framing-camera-limited, FZP-image resolution of ∼3 µm-4 µm.

Constraining physical models at gigabar pressures.

High-energy-density (HED) experiments in convergent geometry are able to test physical models at pressures beyond hundreds of millions of atmospheres. The measurements from these experiments are generally highly integrated and require unique analysis techniques to procure quantitative information. This work describes a methodology to constrain the physics in convergent HED experiments by adapting the methods common to many other fields of physics. As an example, a mechanical model of an imploding shell is constrained by data from a thin-shelled direct-drive exploding-pusher experiment on the OMEGA laser system using Bayesian inference, resulting in the reconstruction of the shell dynamics and energy transfer during the implosion. The model is tested by analyzing synthetic data from a one-dimensional hydrodynamics code and is sampled using a Markov chain Monte Carlo to generate the posterior distributions of the model parameters. The goal of this work is to demonstrate a general methodology that can be used to draw conclusions from a wide variety of HED experiments.

Energy Flow in Thin Shell Implosions and Explosions.

Energy flow and balance in convergent systems beyond petapascal energy densities controls the fate of late-stage stars and the potential for controlling thermonuclear inertial fusion ignition. Time-resolved x-ray self-emission imaging combined with a Bayesian inference analysis is used to describe the energy flow and the potential information stored in the rebounding spherical shock at 0.22 PPa (2.2 Gbar or billions of atmospheres pressure). This analysis, together with a simple mechanical model, describes the trajectory of the shell and the time history of the pressure at the fuel-shell interface, ablation pressure, and energy partitioning including kinetic energy of the shell and internal energy of the fuel. The techniques used here provide a fully self-consistent uncertainty analysis of integrated implosion data, a thermodynamic-path independent measurement of pressure in the petapascal range, and can be used to deduce the energy flow in a wide variety of implosion systems to petapascal energy densities.