The Formation of Globular Cluster Systems in Massive Elliptical Galaxies: Globular Cluster Multimodality from Radial Variation of Stellar Populations
Bibliographic record
Abstract
The most massive elliptical galaxies show a prominent multimodality in their globular cluster system color distributions. Understanding the mechanisms that lead to multiple globular cluster subpopulations is essential for a complete picture of massive-galaxy formation. By assuming that globular cluster formation traces the total star formation and taking into account the radial variations in the composite stellar populations predicted by the Pipino and Matteucci multizone photochemical evolution code, we compute the distribution of globular cluster properties as a function of galactocentric radius. We compare our results to the spectroscopic measurements of globular clusters in nearby early-type galaxies by Puzia and coworkers and show that the observed multimodality in globular cluster systems of massive ellipticals can be ascribed to the radial variation in the mix of stellar populations. Our model predicts the presence of a super-metal-rich population of globular clusters in the most massive elliptical galaxies, which is in very good agreement with the spectroscopic observations. The size of this high-metallicity population scales with galaxy mass, in the sense that more massive galaxies host larger such cluster populations. We predict an increase of mean metallicity of the globular cluster systems with host galaxy mass, and forecast that older clusters exhibit lower metallicities and higher α/Fe ratios. We find that a nonlinear color-metallicity relation may be partly responsible for a color multimodality. On the other hand, the formation of globular clusters from subsequently accreted gas, either with primordial abundances or solar metallicity, delivers model predictions that are at variance with the observations.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".