High accuracy radiative data for plasma opacities<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
Opacity, which gives the measure of the radiation transport in plasmas, is caused by the repeated absorption and emission of the propagating radiation by the constituent plasma elements. Microscopically, opacity (κ) depends mainly on two radiative processes: (i) photo-excitation (bound-bound transition) and (ii) photo-ionization (bound-free transition) in addition to electron-photon scattering. The monochromatic opacity κ(ν) at photon frequency ν is determined by the atomic parameters, oscillator strengths (f), and photo-ionization cross sections (σPI). However, total monochromatic opacity is obtained from summed contributions of all possible transitions from all ionization stages of all elements in the source. The calculation of accurate parameters for such a large number of transitions has been the main problem for obtaining accurate opacities. The overall mean opacity, such as the Rosseland mean opacity (κR), depends also on the physical conditions, such as temperature, density, elemental abundances, and equation of state. The necessity for high-precision calculations for opacities may be exemplified by the existing problems, such as the determination of solar elemental abundances. With new computational developments under the Iron Project, we are able to calculate more accurate atomic parameters, such as oscillator strengths for large number of transitions using the relativistic Breit–Pauli R-matrix (BPRM) method. We are finding new features in photo-ionization, such as the existence of extensive and dominant resonant structures in the high-energy region not studied before. These new data should provide more accurate opacities in high-temperature plasmas and can be used to investigate the well-known solar abundance problem.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".