Testing the tidal stripping scenario of ultracompact dwarf galaxy formation by using internal properties
Bibliographic record
Abstract
ABSTRACT We use the hydrodynamical EAGLE simulation to test if ultracompact dwarf galaxies (UCDs) can form by tidal stripping by predicting the ages and metallicities of tidally stripped galaxy nuclei in massive galaxy clusters, and compare these results to compiled observations of age and metallicities of observed UCDs. We further calculate the colours of our sample of simulated stripped nuclei using single stellar population models and compare these colours to observations of UCDs in the Virgo Cluster. We find that the ages of observed UCDs are consistent with simulated stripped nuclei, with both groups of objects having a mean age >9 Gyr. Both stripped nuclei and UCDs follow a similar mass–metallicity relationship, and the metallicities of observed UCDs are consistent with those of simulated stripped nuclei for objects with M > 107 M⊙. The colours of observed UCDs are also consistent with our simulated stripped nuclei, for objects with M > 107 M⊙, with more massive objects being redder. We find that the colours of stripped nuclei exhibit a bimodal red and blue distribution that can be explained by the dependency of colour on age and metallicity, and by the mass–colour relation. We additionally find that our low-mass stripped nuclei sample is consistent with the colour of blue globular clusters. We conclude that the internal properties of simulated nuclei support the tidal stripping model of UCD 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".