Bibliographic record
Abstract
Context. Relaxing the assumption of local thermodynamic equilibrium (LTE) in modelling stellar spectra is a necessary step to determine chemical abundances to better than about 10% in late-type stars. Aims. We describe our multi-element (Na, Mg, K, and Ca) non-LTE (NLTE) calculations, which can be applied to the APOGEE survey. Methods. The new version of TLUSTY allows for the calculation of restricted NLTE in cool stars using pre-calculated opacity tables. We demonstrate that TLUSTY gives consistent results with MULTI, a well-tested code for NLTE in cool stars. We used TLUSTY to perform LTE and a series of NLTE calculations that simultaneously used all combinations of one, two, three and four of the elements in NLTE. Results. We take into account that departures from LTE in one element can affect others through changes in the opacities of Na, Mg, K, and Ca. We find that atomic Mg, which provides strong UV opacity and exhibits significant departures from LTE in the low-energy states, can affect the NLTE populations of Ca, leading to abundance corrections as large as 0.07 dex. The differences in the derived abundances between the single-element and the multi-element cases can exceed those between the single-element NLTE determinations and an LTE analysis. We therefore caution that this is not always a second-order effect. Based on detailed tests for three stars with reliable atmospheric parameters (Arcturus, Procyon, and the Sun), we conclude that our NLTE calculations provide abundance corrections that can in the optical amount to 0.1, 0.2, and 0.7 dex for Ca, Na and K, but LTE is a good approximation for Mg. In the H-band, NLTE corrections are much smaller and always lower than 0.1 dex. The derived NLTE abundances in the optical and in the IR are consistent. In all three stars, NLTE line profiles fit the observations better than the LTE counterparts for all four elements. Conclusions. The atomic elements in ionisation stages where over-ionisation is an important NLTE mechanism are likely affected by departures from LTE in Mg. Particular care must be taken with the collisions that are adopted for high-lying levels when NLTE profiles of lines in the H-band are calculated. The derived NLTE corrections in the optical and in the H-band differ, but the derived NLTE abundances are consistent between the two spectral regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".