Probing ultrafast laser plasma processes inside solids with resonant small-angle x-ray scattering
Bibliographic record
Abstract
Extreme states of matter exist throughout the universe, e.g., inside planetary cores, stars, or astrophysical jets. Such conditions can be generated in the laboratory in the interaction of powerful lasers with solids. Yet, the measurement of the subsequent plasma dynamics with regard to density, temperature, and ionization is a major experimental challenge. However, ultrashort x-ray pulses provided by x-ray free electron lasers (XFELs) allow for dedicated studies, which are highly relevant to study laboratory astrophysics, laser-fusion research, or laser-plasma-based particle acceleration. Here we report on experiments that employ a novel ultrafast method, which allows us to simultaneously access temperature, ionization state, and nanometer scale expansion dynamics in high-intensity, laser-driven, solid-density plasmas with a single x-ray detector. Using this method, we gain access to the expansion dynamics of a buried layer in compound samples, and we measure opacity changes arising from bound-bound resonance transitions in highly ionized copper. The presence of highly ionized copper leads to a temperature estimate of at least 2 million Kelvin already after the first 100 fs following the high-intensity laser irradiation. More specifically, we make use of asymmetries in small-angle x-ray scattering (SAXS) patterns, which arise from different spatial distributions of absorption and scattering cross sections in nanostructured grating samples when we tune an XFEL to atomic resonant energies of copper. Thereby, changes in asymmetry can be connected with the evolution of the plasma expansion and ionization dynamics. The potential of XFEL-based resonant SAXS to obtain three-dimensional ultrafast, nanoscopic information on density and opacity may offer a unique path for the characterization of dynamic processes in high energy density plasmas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".