A maximum stellar surface density in dense stellar systems
Bibliographic record
Abstract
Abstract We compile observations of the surface mass density profiles of dense stellar systems, including globular clusters in the Milky Way and nearby galaxies, massive star clusters in nearby starbursts, nuclear star clusters in dwarf spheroidals and late-type discs, ultra-compact dwarfs, and galaxy spheroids spanning the range from low-mass ‘cusp’ bulges and ellipticals to massive ‘core’ ellipticals. We show that in all cases the maximum stellar surface density attained in the central regions of these systems is similar, Σmax ∼ 1011 M⊙ kpc−2 (∼20 g cm−2), despite the fact that the systems span ∼7 orders of magnitude in total stellar mass M* and ∼5 in effective radius Re, and have a wide range in effective surface density M*/R2e. The surface density limit is reached on a wide variety of physical scales in different systems and is thus not a limit on three-dimensional stellar density. Given the very different formation mechanisms involved in these different classes of objects, we argue that a single piece of physics likely determines Σmax. The radiation fields and winds produced by massive stars can have a significant influence on the formation of both star clusters and galaxies, while neither supernovae nor black hole accretion is important in star cluster formation. We thus conclude that feedback from massive stars likely accounts for the observed Σmax, plausibly because star formation reaches an Eddington-like flux that regulates the growth of these diverse systems. This suggests that current models of galaxy formation, which focus on feedback from supernovae and active galactic nuclei, are missing a crucial ingredient.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".