The Next Generation Virgo Cluster Survey. XXIII. Fundamentals of Nuclear Star Clusters over Seven Decades in Galaxy Mass
Bibliographic record
Abstract
Abstract Using deep, high-resolution optical imaging from the Next Generation Virgo Cluster Survey, we study the properties of nuclear star clusters (NSCs) in a sample of nearly 400 quiescent galaxies in the core of Virgo with stellar masses 105 ≲ / ≲ 1012. The nucleation fraction reaches a peak value f n ≈ 90% for ≈ 109 galaxies and declines for both higher and lower masses, but nuclei populate galaxies as small as ≈ 5 × 105 . Comparison with literature data for nearby groups and clusters shows that at the low-mass end nucleation is more frequent in denser environments. The NSC mass function peaks at M NSC ≈ 7 × 105 , a factor 3–4 times larger than the turnover mass for globular clusters (GCs). We find a nonlinear relation between the stellar masses of NSCs and those of their host galaxies, with a mean nucleus-to-galaxy mass ratio that drops to M NSC/M ⋆ ≈ 3.6 × 10−3 for ≈ 5 × 109 galaxies. Nuclei in both more and less massive galaxies are much more prominent: at the low-mass end, where nuclei are nearly 50% as massive as their hosts. We measure an intrinsic scatter in NSC masses at a fixed galaxy stellar mass of 0.4 dex, which we interpret as evidence that the process of NSC growth is significantly stochastic. At low galaxy masses we find a close connection between NSCs and GC systems, including very similar occupation distributions and comparable total masses. We discuss these results in the context of current dissipative and dissipationless models of NSC 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".