Cosmological Evolution of Supergiant Star‐forming Clouds
Bibliographic record
Abstract
In an exploration of the birthplaces of globular clusters, we present a careful examination of the formation of self-gravitating gas clouds within assembling dark matter haloes in a hierarchical cosmological model. Our high-resolution smoothed particle hydrodynamical simulations are designed to determine whether or not hypothesized supergiant molecular clouds (SGMCs) form and, if they do, to determine their physical properties and mass spectra. It was suggested in earlier work that clouds with a median mass of several 10 8 M ☉ are expected to assemble during the formation of a galaxy and that globular clusters form within these SGMCs. Our simulations show that clouds with the predicted properties are indeed produced as smaller clouds collide and agglomerate within the merging dark matter haloes of our cosmological model. We find that the mass spectrum of these clouds obeys the same power-law form, dN / dM ∝ M -1.7±0.1 , observed for globular clusters, molecular clouds, and their internal clumps in galaxies, and predicted for the supergiant clouds in which globular clusters may form. We follow the evolution and physical properties of gas clouds within small dark matter haloes up to z = 1, after which prolific star formation is expected to occur. Finally, we discuss how our results may lead to more physically motivated "rules" for star formation in cosmological simulations of galaxy formation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".