NON-LTE MODELING OF THE NEAR-ULTRAVIOLET BAND OF LATE-TYPE STARS
Bibliographic record
Abstract
We investigate the ability of both LTE and non-LTE (NLTE) models to fit the near-UV band absolute flux distribution, f λ (λ), and individual spectral line profiles of three standard stars for which high-quality spectrophotometry and high-resolution spectroscopy are available: The Sun (G2 V), Arcturus (K2 III), and Procyon (F5 IV-V). We investigate (1) the effect of the choice of atomic line list on the ability of NLTE models to fit the near-UV band f λ level, (2) the amount of a hypothesized continuous thermal absorption extinction source required to allow NLTE models to fit the observations, and (3) the semiempirical temperature structure, T kin (log τ 5000 ), required to fit the observations with NLTE models and standard continuous near-UV extinction. We find that all models that are computed with high-quality atomic line lists predict too much flux in the near-UV band for Arcturus, but fit the warmer stars well. The variance among independent measurements of the solar irradiance in the near-UV is sufficiently large that we cannot definitely conclude that models predict too much near-UV flux, in contrast to other recent results. We surmise that the inadequacy of current atmospheric models of K giants in the near-UV band is best addressed by hypothesizing that there is still missing continuous thermal extinction, and that the missing near-UV extinction becomes more important with decreasing effective temperature for spectral classes later than early G, suggesting a molecular origin.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".