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 Msun, 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 ("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 mass-metallicity relation. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".