INSIGHTS INTO PRE-ENRICHMENT OF STAR CLUSTERS AND SELF-ENRICHMENT OF DWARF GALAXIES FROM THEIR INTRINSIC METALLICITY DISPERSIONS
Bibliographic record
Abstract
Star clusters are known to have smaller intrinsic metallicity spreads than dwarf galaxies due to their shorter star formation timescales. Here we use individual spectroscopic [Fe/H] measurements of stars in 19 Local Group dwarf galaxies, 13 Galactic open clusters, and 49 globular clusters to show that star cluster and dwarf galaxy linear metallicity distributions are binomial in form, with all objects showing strong correlations between their mean linear metallicity and intrinsic spread in metallicity σ( Z ) 2 . A plot of σ( Z ) 2 versus shows that the correlated relationships are offset for the dwarf galaxies from the star clusters. The common binomial nature of these linear metallicity distributions can be explained with a simple inhomogeneous chemical evolution model, where the star cluster and dwarf galaxy behavior in the diagram is reproduced in terms of the number of enrichment events, covering fraction, and intrinsic size of the enriched regions. The inhomogeneity of the self-enrichment sets the slope for the observed dwarf galaxy correlation. The offset of the star cluster sequence from that of the dwarf galaxies is due to pre-enrichment, and the slope of the star cluster sequence represents the remnant signature of the self-enriched history of their host galaxies. The offset can be used to separate star clusters from dwarf galaxies without a priori knowledge of their luminosity or dynamical mass. The application of the inhomogeneous model to the relationship provides a numerical formalism to connect the self-enrichment and pre-enrichment between star clusters and dwarf galaxies using physically motivated chemical enrichment parameters. Therefore we suggest that the relationship can provide insight into what drives the efficiency of star formation and chemical evolution in galaxies, and is an important prediction for galaxy simulation models to reproduce.
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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 | 0.002 |
| 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 | 0.001 | 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".