The abundance of ultra-diffuse galaxies from groups to clusters UDGs are relatively more common in more massive haloes
Bibliographic record
Abstract
In recent years, multiple studies have reported substantial populations of large, low-surface-brightness galaxies in local galaxy clusters. Various theories that aim to explain the presence of such ultra-diffuse galaxies (UDGs) have since been proposed. A key question that will help to differentiate between models is whether UDGs have counterparts in lower-mass host haloes, and what their abundance as a function of halo mass is. In this study we extend our previous study of UDGs in galaxy clusters to galaxy groups. We measure the abundance of UDGs in 325 spectroscopically-selected groups from the Galaxy And Mass Assembly (GAMA) survey. We make use of the overlapping imaging from the ESO Kilo-Degree Survey (KiDS), from which we can identify galaxies with mean surface brightnesses within their effective radii down to ~25.5 mag arcsec$^{-2}$ in the r-band. We are able to measure a significant overdensity of UDGs (with sizes r_eff > 1.5 kpc) in galaxy groups down to M200=10^12 Msun, a regime where approximately only 1 in 10 groups contains a UDG that we can detect. We combine measurements of the abundance of UDGs in haloes that cover three orders of magnitude in halo mass, finding that their numbers scale quite steeply with halo mass; N_UDG (R
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".