Extremely metal-poor stars in the Fornax and Carina dwarf spheroidal galaxies
Bibliographic record
Abstract
We present our analysis of VLT/UVES and X-shooter observations of six very metal-poor stars, including four stars at [Fe/H] ≈ −3 in the Fornax and Carina dwarf spheroidal (dSph) galaxies. To date, this metallicity range in these two galaxies has not yet been investigated fully, or at all in some cases. The chemical abundances of 25 elements are presented, based on 1D and local thermodynamic equilibrium (LTE) model atmospheres. We discuss the different elemental groups, and find thatα- and iron-peak elements in these two systems are generally in good agreement with the Milky Way halo at the same metallicity. Our analysis reveals that none of the six stars we studied exhibits carbon enhancement, which is noteworthy given the prevalence of carbon-enhanced metal-poor stars withouts-process enhancement (CEMP-no) in the Galaxy at similarly low metallicities. Our compilation of literature data shows that the fraction of CEMP-no stars in dSph galaxies is significantly lower than in the Milky Way, and than in ultra-faint dwarf galaxies. Furthermore, we report the discovery of the lowest metallicity, [Fe/H] = −2.92,r-process rich (r-I) star in a dSph galaxy. This star, fnx_06_019, has [Eu/Fe] = +0.8, and also shows enhancement of La, Nd, and Dy, [X/Fe] > +0.5. Our new data in Carina and Fornax help populate the extremely low metallicity range in dSph galaxies, and add to the evidence of a low fraction of CEMP-no stars in these systems.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".