K-shell spectroscopy of Au plasma generated with a short-pulse laser<sup>1</sup>This article is part of a Special Issue on the 10th International Colloquium on Atomic Spectra and Oscillator Strengths for Astrophysical and Laboratory Plasmas.
Bibliographic record
Abstract
The production of X-rays from electron transitions into K-shell vacancies (Kα,β) emission) is a well-known process in atomic physics and has been extensively studied as a plasma diagnostic in low- and mid-Z materials. However, X-ray spectra from near neutral high-Z ions are very complex, and their interpretation requires the use of state-of-the-art atomic calculations. In this experiment, the Titan laser system at Lawrence Livermore National Laboratory was used to deliver an approximately 350 J laser pulse, with a 10 ps duration and a wavelength of 1054 nm, to a gold (Au) target. A transparent bent quartz crystal spectrometer with a hard X-ray energy window, ranging from 17 to 102 keV, was used to measure the emission spectrum. Kα1,α2 and Kβ1,γ1 transitions were observed over a range of target sizes. Additionally, a series of shots were conducted with a pre-ionizing long pulse (3 ns, 1–10 J, 527 nm) on the backside of the target. FLYCHK, an atomic non-LTE code, designed to provide ionization and population distributions, was used to model the experiment. Kα/Kβ ratios were found to be in good agreement with the predicted value for room temperature Au targets.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".