The formation of the Milky Way halo and its dwarf satellites; a NLTE-1D abundance analysis
Bibliographic record
Abstract
We present a homogeneous set of accurate atmospheric parameters for a complete sample of very and extremely metal-poor stars in the dwarf spheroidal galaxies (dSphs) Sculptor, Ursa Minor, Sextans, Fornax, Boötes I, Ursa Major II, and Leo IV. We also deliver a Milky Way (MW) comparison sample of giant stars covering the − 4 < [Fe/H] < − 1.7 metallicity range. We show that, in the [Fe/H] ≿ − 3.7 regime, the non-local thermodynamic equilibrium (NLTE) calculations with non-spectroscopic effective temperature (Teff) and surface gravity (log g) based on the photometric methods and known distance provide consistent abundances of the Fe i and Fe ii lines. This justifies the Fe i/Fe ii ionisation equilibrium method to determine log g for the MW halo giants with unknown distance. The atmospheric parameters of the dSphs and MW stars were checked with independent methods. In the [Fe/H] > − 3.5 regime, the Ti i/Ti ii ionisation equilibrium is fulfilled in the NLTE calculations. In the log g − Teff plane, all the stars sit on the giant branch of the evolutionary tracks corresponding to [Fe/H] = − 2 to − 4, in line with their metallicities. For some of the most metal-poor stars of our sample, we achieve relatively inconsistent NLTE abundances from the two ionisation stages for both iron and titanium. We suggest that this is a consequence of the uncertainty in the Teff-colour relation at those metallicities. The results of this work provide the basis for a detailed abundance analysis presented in a companion paper.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".