Formation of ultra-diffuse galaxies in the field and in galaxy groups
Bibliographic record
Abstract
ABSTRACT We study ultra-diffuse galaxies (UDGs) in zoom in cosmological simulations, seeking the origin of UDGs in the field versus galaxy groups. We find that while field UDGs arise from dwarfs in a characteristic mass range by multiple episodes of supernova feedback (Di Cintio et al.), group UDGs may also form by tidal puffing up and they become quiescent by ram-pressure stripping. The field and group UDGs share similar properties, independent of distance from the group centre. Their dark-matter haloes have ordinary spin parameters and centrally dominant dark-matter cores. Their stellar components tend to have a prolate shape with a Sérsic index n ∼ 1 but no significant rotation. Ram pressure removes the gas from the group UDGs when they are at pericentre, quenching star formation in them and making them redder. This generates a colour/star-formation-rate gradient with distance from the centre of the dense environment, as observed in clusters. We find that ∼20 per cent of the field UDGs that fall into a massive halo survive as satellite UDGs. In addition, normal field dwarfs on highly eccentric orbits can become UDGs near pericentre due to tidal puffing up, contributing about half of the group-UDG population. We interpret our findings using simple toy models, showing that gas stripping is mostly due to ram pressure rather than tides. We estimate that the energy deposited by tides in the bound component of a satellite over one orbit can cause significant puffing up provided that the orbit is sufficiently eccentric. We caution that while the simulations produce UDGs that match the observations, they under-produce the more compact dwarfs in the same mass range, possibly because of the high threshold for star formation or the strong feedback.
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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 | 0.000 | 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) | 0.002 | 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".