The Next Generation Virgo Cluster Survey (NGVS). XXX. Ultra-diffuse Galaxies and Their Globular Cluster Systems
Bibliographic record
Abstract
Abstract We present a study of ultra-diffuse galaxies (UDGs) in the Virgo Cluster based on deep imaging from the Next Generation Virgo Cluster Survey. Applying a new definition for the UDG class based on galaxy scaling relations, we define samples of 44 and 26 UDGs using expansive and restrictive selection criteria, respectively. Our UDG sample includes objects that are significantly fainter than previously known UDGs: i.e., more than half are fainter than mag arcsec−2. The UDGs in Virgo’s core region show some evidence for being structurally distinct from “normal” dwarf galaxies, but this separation disappears when considering the full sample of galaxies throughout the cluster. UDGs are more centrally concentrated in their spatial distribution than other Virgo galaxies of similar luminosity, while their morphologies demonstrate that at least some UDGs owe their diffuse nature to physical processes—such as tidal interactions or low-mass mergers—that are at play within the cluster environment. The globular cluster (GC) systems of Virgo UDGs have a wide range in specific frequency (S N ), with a higher mean S N than “normal” Virgo dwarfs, but a lower mean S N than Coma UDGs at fixed luminosity. Their GCs are predominantly blue, with a small contribution from red clusters in the more massive UDGs. The combined GC luminosity function is consistent with those observed in dwarf galaxies, showing no evidence of being anomalously luminous. The diversity in their morphologies and their GC properties suggests no single process has given rise to all objects within the UDG class. Based on the available evidence, we conclude that UDGs are simply those systems that occupy the extended tails of the galaxy size and surface brightness distributions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".