MODELING THE NEAR-UV BAND OF GK STARS. II. NON-LTE MODELS
Bibliographic record
Abstract
We present a grid of atmospheric models and synthetic spectral energy distributions (SEDs) for late-type dwarfs and giants of solar and 1/3 solar metallicity with many opacity sources computed in self-consistent non-local thermodynamic equilibrium (NLTE), and compare them to the LTE grid of Short & Hauschildt (Paper I). We describe, for the first time, how the NLTE treatment affects the thermal equilibrium of the atmospheric structure ( T (τ) relation) and the SED as a finely sampled function of T eff , log g , and [A/H] among solar metallicity and mildly metal-poor red giants. We compare the computed SEDs to the library of observed spectrophotometry described in Paper I across the entire visible band, and in the blue and red regions of the spectrum separately. We find that for the giants of both metallicities, the NLTE models yield best-fit T eff values that are 30–90 K lower than those provided by LTE models, while providing greater consistency between log g values, and, for Arcturus, T eff values, fitted separately to the blue and red spectral regions. There is marginal evidence that NLTE models give more consistent best-fit T eff values between the red and blue bands for earlier spectral classes among the solar metallicity GK giants than they do for the later classes, but no model fits the blue-band spectrum well for any class. For the two dwarf spectral classes that we are able to study, the effect of NLTE on derived parameters is less significant. We compare our derived T eff values to several other spectroscopic and photometric T eff calibrations for red giants, including one that is less model dependent based on the infrared flux method (IRFM). We find that the NLTE models provide slightly better agreement to the IRFM calibration among the warmer stars in our sample, while giving approximately the same level of agreement for the cooler stars.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".