The formation of ultradiffuse galaxies in clusters
Bibliographic record
Abstract
ABSTRACT We use the IllustrisTNG cosmological hydrodynamical simulation to study the formation of ultradiffuse galaxies (UDGs) in galaxy clusters. We supplement the simulations with a realistic mass–size relation for galaxies at the time of infall into the cluster, as well as an analytical model to describe the tidally induced evolution of their stellar mass, velocity dispersion, and size. The model assumes ‘cuspy’ NFW haloes and, contrary to recent claims, has no difficulty reproducing the observed number of UDGs in clusters. Our results further suggest that the UDG population consists of a mixture of ‘normal’ low surface brightness galaxies such as those found in the field (‘born’ UDGs, or B-UDGs), as well as a distinct population that owe their large size and low surface brightness to the effects of cluster tides (‘tidal’, or T-UDGs). The simulations indicate that T-UDGs entered the cluster earlier and should be more prevalent than B-UDGs near the cluster centres. T-UDGs should also have, at given stellar mass, lower velocity dispersion, higher metallicities, and lower dark matter content than B-UDGs. Our results suggest that systems like DF-44 are consistent with having been born as UDGs, while others such as DF2, DF4, and VLSB-D are possibly extreme T-UDG examples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".