The Carina Project. V. The Impact of NLTE Effects on the Iron Content
Bibliographic record
Abstract
We have performed accurate iron abundance measurements for 44 red giants (RGs) in the Carina dwarf spheroidal (dSph) galaxy. We used archival, high-resolution spectra ( R ∼ 38,000) collected with UVES at ESO/VLT either in slit mode (five RGs) or in fiber mode (39 RGs, FLAMES/GIRAFFE-UVES). The sample is more than a factor of 4 larger than any previous spectroscopic investigation of stars in dSphs based on high-resolution ( R ≥38000) spectra. We did not impose the ionization equilibrium between neutral and singly ionized iron lines. The effective temperatures and the surface gravities were estimated by fitting stellar isochrones in the V , B - V color-magnitude diagram. To measure the iron abundance of individual lines we applied the LTE spectrum-synthesis fitting method using MARCS model atmospheres of appropriate metallicity. For the 27 stars for which we measured both Fe I and Fe II abundances, we found evidence of NLTE effects between neutral and singly ionized iron abundances. The difference is ∼0.1 dex, on average, but steadily increases when moving from the metal-rich to the metal-poor regime. Moreover, the two metallicity distributions differ at the 97% confidence level. Assuming that the Fe II abundances are minimally affected by NLTE effects, we corrected the Fe I stellar abundances using a linear fit between Fe I and Fe II stellar abundance determinations. We found that the Carina metallicity distribution based on the corrected Fe I abundances (44 RGs) has a weighted mean metallicity of [Fe/H] = -1.80 and a weighted standard deviation of σ = 0.24 dex. The Carina metallicity distribution based on the Fe II abundances (27 RGs) gives similar estimates ([Fe/H] = -1.72, σ = 0.24 dex). The current weighted mean metallicities are slightly more metal-poor when compared with similar estimates available in the literature. Furthermore, when we restricted our analysis to stars with the most accurate iron abundances, ∼20 Fe I and at least three Fe II measurements (15 stars), we found that the range in iron abundances covered by Carina RGs (∼1 dex) agreed quite well with similar estimates based on high-resolution spectra; however, it is a factor of 2–3 smaller than abundance estimates based on the near-infrared calcium triplet. This finding supports previous estimates based on photometric metallicity indicators.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.035 |
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".