Atomic data calculations for Au <scp>i</scp>–Au <scp>iii</scp> and exploration in the application of collisional-radiative theory to laboratory and neutron star merger plasmas
Bibliographic record
Abstract
ABSTRACT Neutron binary star mergers have long been proposed as sufficiently neutron rich environments that could support the synthesis of rapid neutron capture elements (r-process elements) such as gold. However, the literature reveals that beyond neutral and singly ionized systems, there is an incompleteness of atomic data for the remaining ion stages of importance for mergers. In this work, we report on relativistic atomic structure calculations for Au i–Au iii using the grasp0 codes. Comparisons to calculations using the Flexible Atomic Code suggest uncertainties on average of 9.2 per cent, 5.7 per cent, and 3.8 per cent for Au i–Au iii level energies. Agreement around ∼50 per cent is achieved between our computed A-values and those in the literature, where available. Using the grasp0 structure of Au i, we calculated electron-impact excitation rate coefficients and use a collisional-radiative model to explore the excitation dynamics and line ratio diagnostics possible in neutron star merger environments. We find that proper accounting of metastable populations is critical for extracting useful information from ultraviolet–visible line ratio diagnostics of Au i. As a test of our data, we applied our electron-impact data to study a gold hollow cathode spectrum in the literature and diagnosed the plasma conditions as Te = 3.1 ± 1.2 eV and $n_\textrm {e} = 2.7^{+1.3}_{-0.9}\times 10^{13}$ cm−3.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".