Atmospheric Models of Red Giants with Massive‐Scale Non–Local Thermodynamic Equilibrium
Bibliographic record
Abstract
We present plane-parallel and spherical LTE and non-LTE (NLTE) atmospheric models of a variety of stellar parameters of the red giant star Arcturus (α Boo, HD 124897, HR 5340) and study their ability to fit the measured absolute flux distribution. Our NLTE models include tens of thousands of the strongest lines in NLTE, and we investigate separately the effects of treating the light metals and the Fe group elements Fe and Ti in NLTE. We find that the NLTE effects of Fe group elements on the model structure and F λ distribution are much more important than the NLTE effects of all the light metals combined, and serve to substantially increase the violet and near-UV F λ levels as a result of NLTE Fe overionization. Both the LTE and NLTE models predict significantly more flux in the blue and UV bands than is observed. We find that within the moderately metal-poor metallicity range, the effect of NLTE on the overall UV flux level decreases with decreasing metallicity. These results suggest that there may still be important UV opacity missing from the models. We find that models of solar metallicity giants of spectral type similar to that of Arcturus fit well the observed flux distributions of those stars from the red to the near-UV band. This suggests that the blue and near-UV flux discrepancy is metallicity dependent, increasing with decreasing metallicity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".