The Dependence of Iron-rich Metal-poor Star Occurrence on Galactic Environment Supports an Origin in Thermonuclear Supernova Nucleosynthesis
Bibliographic record
Abstract
Abstract It has been suggested that a class of chemically peculiar metal-poor stars called iron-rich metal-poor (IRMP) stars formed from molecular cores with metal contents dominated by thermonuclear supernova nucleosynthesis. If this interpretation is accurate, then IRMP stars should be more common in environments where thermonuclear supernovae were important contributors to chemical evolution. Conversely, IRMP stars should be less common in environments where thermonuclear supernovae were not important contributors to chemical evolution. At constant [Fe/H] ≲ −1, the Milky Way’s satellite classical dwarf spheroidal (dSph) galaxies and the Magellanic Clouds have lower [α/Fe] than the Milky Way field and globular cluster populations. This difference is thought to demonstrate the importance of thermonuclear supernova nucleosynthesis for the chemical evolution of the Milky Way’s satellite classical dSph galaxies and the Magellanic Clouds. We use data from the Sloan Digital Sky Survey Apache Point Observatory Galactic Evolution Experiment and Gaia to infer the occurrence of IRMP stars in the Milky Way’s satellite classical dSph galaxies η dSph and the Magellanic Clouds η Mag, as well as in the Milky Way field η MWF and globular cluster populations η MWGC. In order of decreasing occurrence, we find η dSph = 0.07 − 0.02 + 0.02 , η Mag = 0.037 − 0.006 + 0.007 , η MWF = 0.0013 − 0.0005 + 0.0006 , and a 1σ upper limit η MWGC < 0.00057. These occurrences support the inference that IRMP stars formed in environments dominated by thermonuclear supernova nucleosynthesis and that the time lag between the formation of the first and second stellar generations in globular clusters was longer than the thermonuclear supernova delay time.
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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 | 0.001 |
| 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) | 0.004 | 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".