Extremely metal-poor stars in classical dwarf spheroidal galaxies: Fornax, Sculptor, and Sextans
Bibliographic record
Abstract
We present the results of a dedicated search for extremely metal-poor stars in the Fornax, Sculptor, and Sextans dSphs. Five stars were selected from two earlier VLT/Giraffe and HET/HRS surveys and subsequently followed up at high spectroscopic resolution with VLT/UVES. All of them turned out to have [Fe/H] ≲ −3 and three stars are below [Fe/H] ~ −3.5. This constitutes the first evidence that the classical dSphs Fornax and Sextans join Sculptor in containing extremely metal-poor stars and suggests that all of the classical dSphs contain extremely metal-poor stars. One giant in Sculptor at [Fe/H] = −3.96 ± 0.06 is the most metal-poor star ever observed in an external galaxy. We carried out a detailed analysis of the chemical abundances of the α, iron peak, and the heavy elements, and we performed a comparison with the Milky Way halo and the ultra faint dwarf stellar populations. Carbon, barium, and strontium show distinct features characterized by the early stages of galaxy formation and can constrain the origin of their nucleosynthesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".