On the use of the Axelrod formula for thermal electron collisions in astrophysical modelling
Bibliographic record
Abstract
The Axelrod approximation is widely used in astrophysical modelling codes to evaluate electron-impact excitation effective collision strengths for forbidden transitions. Approximate methods such as this are a necessity for many heavy elements with open shells where collisional data is either non existent or sparse as the use of more robust methods prove prohibitively expensive. Atomic data for such forbidden transitions are essential for producing full collisional radiative models that do not assume Local-Thermodynamic-Equilibrium (LTE). In this short work we present the re-optimization the simple Axelrod formula for a large number of R -matrix data sets, ranging from Fe and Ni to the first r-process peak elements of Sr, Y and Zr, to higher Z systems Te, W, Pt and Au. We show that the approximate treatment of forbidden transitions can be a significant source of inaccuracy in such collisional radiative models. We find a large variance of the optimized coefficients for differing systems and charge states, although some general trends can be seen based on the orbital structure of the ground-state-configurations. These trends could potentially inform better estimates for future calculations for elements where R -matrix data is not available.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".