STOCHASTIC SELF-ENRICHMENT, PRE-ENRICHMENT, AND THE FORMATION OF GLOBULAR CLUSTERS
Bibliographic record
Abstract
We develop a model for stochastic pre-enrichment and self-enrichment in globular clusters (GCs) during their formation process. GCs beginning their formation have an initial metallicity determined by the pre-enrichment of their surrounding protocloud, but can also undergo internal self-enrichment during formation. Stochastic variations in metallicity arise because of the finite numbers of supernova. We construct an analytic formulation of the combined effects of pre-enrichment and self-enrichment and use Monte Carlo models to verify that the model accurately encapsulates the mean metallicity and metallicity spread among real GCs. The predicted metallicity spread due to self-enrichment alone, a robust prediction of the model, is much smaller than the observed spread among real GCs. This result rules out self-enrichment as a significant contributor to the metal content in most GCs, leaving pre-enrichment as the viable alternative. Self-enrichment can, however, be important for clusters with masses well above 10 6 M ☉ , which are massive enough to hold in a significant fraction of their SN ejecta even without any external pressure confinement. This transition point corresponds well to the mass at which a mass–metallicity relationship (MMR, "blue tilt") appears in the metal-poor cluster sequence in many large galaxies. We therefore, suggest that self-enrichment is the primary driver for the MMR. Other predictions from our model are that the cluster-to-cluster metallicity spread decreases amongst the highest mass clusters; and that the red GC sequence should also display a more modest mass–metallicity trend if it can be traced to similarly high mass.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".