Concurrent formation of supermassive stars and globular clusters: implications for early self-enrichment
Bibliographic record
Abstract
We present a model for the concurrent formation of globular clusters (GCs) and supermassive stars (SMSs, 10 3 M ) to address the origin of the HeCNONaMgAl abundance anomalies in GCs.GCs form in converging gas flows and accumulate low-angular momentum gas, which accretes on to protostars.This leads to an adiabatic contraction of the cluster and an increase of the stellar collision rate.A SMS can form via runaway collisions if the cluster reaches sufficiently high density before two-body relaxation halts the contraction.This condition is met if the number of stars 10 6 and the gas accretion rate 10 5 M Myr -1 , reminiscent of GC formation in high gas-density environments, such as -but not restricted to -the early Universe.The strong SMS wind mixes with the inflowing pristine gas, such that the protostars accrete diluted hot-hydrogen burning yields of the SMS.Because of continuous rejuvenation, the amount of processed material liberated by the SMS can be an order of magnitude higher than its maximum mass.This 'conveyor-belt' production of hot-hydrogen burning products provides a solution to the mass budget problem that plagues other scenarios.Additionally, the liberated material is mildly enriched in helium and relatively rich in other hot-hydrogen burning products, in agreement with abundances of GCs today.Finally, we find a super-linear scaling between the amount of processed material and cluster mass, providing an explanation for the observed increase of the fraction of processed material with GC mass.We discuss open questions of this new GC enrichment scenario and propose observational tests.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".