On the link between nuclear star cluster and globular cluster system mass, nucleation fraction, and environment
Bibliographic record
Abstract
ABSTRACT We present a simple model for the host mass dependence of the galaxy nucleation fraction (fnuc), the galaxy’s nuclear star cluster (NSC) mass, and the mass in its surviving globular clusters (MGC, obs). Considering the mass and orbital evolution of a GC in a galaxy potential, we define a critical mass limit (MGC, lim) above which a GC can simultaneously in-spiral to the galaxy centre due to dynamical friction and survive tidal dissolution, to build-up the NSC. The analytical expression for this threshold mass allows us to model the nucleation fraction for populations of galaxies. We find that the slope and curvature of the initial galaxy size–mass relation is the most important factor (with the shape of the GC mass function a secondary effect) setting the fraction of galaxies that are nucleated at a given mass. The well-defined skew-normal fnuc–Mgal observations in galaxy cluster populations are naturally reproduced in these models, provided there is an inflection in the initial size–mass relation at Mgal ∼ 109.5 M⊙. Our analytical model also predicts limits to the Mgal–MGC, tot and Mgal–MNSC relations which bound the scatter of the observational data. Moreoever, we illustrate how these scaling relations and fnuc vary if the star cluster formation efficiency, GC mass function, galaxy environment, or galaxy size–mass relation are altered. Two key predictions of our model are: (1) galaxies with NSC masses greater than their GC system masses are more compact at fixed stellar mass and (2) the fraction of nucleated galaxies at fixed galaxy mass is higher in denser environments. That a single model framework can reproduce both the NSC and GC scaling relations provides strong evidence that GC in-spiral is an important mechanism for NSC formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".