The Pristine survey – XIV. Chemical analysis of two ultra-metal-poor stars
Bibliographic record
Abstract
ABSTRACT Elemental abundances of the most metal-poor stars reflect the conditions in the early Galaxy and the properties of the first stars. We present a spectroscopic follow-up of two ultra-metal-poor stars ([Fe/H] < −4.0) identified by the survey Pristine: Pristine 221.8781+9.7844 and Pristine 237.8588+12.5660 (hereafter Pr 221 and Pr 237, respectively). Combining data with earlier observations, we find a radial velocity of −149.25 ± 0.27 and −3.18 ± 0.19 km s−1 for Pr 221 and Pr 237, respectively, with no evidence of variability between 2018 and 2020. From a one-dimensional (1D) local thermodynamic equilibrium (LTE) analysis, we measure [Fe/H]LTE = −4.79 ± 0.14 for Pr 221 and −4.22 ± 0.12 for Pr 237, in good agreement with previous studies. Abundances of Li, Na, Mg, Al, Si, Ca, Ti, Fe, and Sr were derived based on the non-LTE (NLTE) line formation calculations. When NLTE effects are included, we measure slightly higher metallicities: [Fe/H]NLTE = −4.40 ± 0.13 and −3.93 ± 0.12, for Pr 221 and Pr 237, respectively. Analysis of the G band yields [C/Fe]1D-LTE ≤ +2.3 and [C/Fe]1D-LTE ≤ +2.0 for Pr 221 and Pr 237. Both stars belong to the low-carbon band. Upper limits on nitrogen abundances are also derived. Abundances for other elements exhibit good agreement with those of stars with similar parameters. Finally, to get insight into the properties of their progenitors, we compare NLTE abundances to theoretical yields of zero-metallicity supernovae (SNe). This suggests that the SNe progenitors had masses ranging from 10.6 to 14.4 M⊙ and low-energy explosions with (0.3–1.2) × 1051 erg.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".