Globular clusters as tracers of the dark matter content of dwarfs in galaxy clusters
Bibliographic record
Abstract
ABSTRACT Globular clusters (GCs) are often used to estimate the dark matter content of galaxies, especially dwarf galaxies, where other kinematic tracers are lacking. These estimates typically assume spherical symmetry and dynamical equilibrium, assumptions that may not hold for the sparse GC population of dwarfs in galaxy clusters. We use a catalogue of GCs tagged on to the Illustris simulation to study the accuracy of GC-based mass estimates. We focus on galaxies in the stellar mass range 108–1011.8 M⊙ identified in nine simulated Virgo-like clusters. Our results indicate that mass estimates are, on average, accurate in systems with GC numbers NGC ≥ 10 and where the uncertainty of individual GC line-of-sight velocities is smaller than the inferred velocity dispersion, σGC. In cases where NGC ≤ 10, however, biases may result, depending on how σGC is computed. We provide calibrations that may help alleviate these biases in methods widely used in the literature. As an application, we find a number of dwarfs with $M_{*} \sim 10^{8.5}\, \mathrm{M}_{\odot }$ – comparable with the ultra-diffuse galaxy NGC 1052-DF2 (DF2), notable for the low σGC of its 10 GCs – that have $\sigma _{\rm GC} \sim 7\!-\!15\, {\rm km \,s}^{-1}$. These DF2 analogues correspond to relatively massive systems at their infall time (M200 ∼ 1–3 × 1011 M⊙), which have retained only 3–17 GCs and have been stripped of more than 95 per cent of their dark matter. Our results suggest that extreme tidal mass loss in otherwise normal dwarf galaxies may be a possible formation channel for ultra-diffuse objects such as DF2.
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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 | 0.004 |
| 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 | 0.000 |
| 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".