A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE)
Bibliographic record
Abstract
Context. Low-surface-brightness galaxies (LSBs) contribute to a significant fraction of all the galaxies in the Universe. Ultra-diffuse galaxies (UDGs) form a subclass of LSBs that has attracted a lot of attention in recent years (although its definition may vary between studies). Although UDGs are found in large numbers in galaxy clusters, groups, and in the field, their formation and evolution are still very much debated. Aims. Using a comprehensive set of multiwavelength data from the NGVS (optical), VESTIGE (Hα narrowband), and GUViCS (UV) surveys, we studied a sample of 64 diffuse galaxies and UDGs in the Virgo cluster to investigate their formation history. Methods. We analyzed the photometric colors and surface-brightness profiles of these galaxies and then compared them to models of galaxy evolution, including ram-pressure stripping (RPS) events to infer any possible strong interactions with the hot cluster gas in the past. Results. While our sample consists mainly of red LSBs, which is typical in cluster environments, we found evidence of a color variation with the cluster-centric distance. Blue, H I-bearing, star-forming diffuse galaxies are found at larger distances from the cluster center than the rest of the sample. The comparison of our models with multifrequency observations suggests that most of the galaxies of the sample might have undergone a strong RPS event in their lifetime, on average 1.6 Gyr ago (with a large dispersion, and RPS still ongoing for some of them). This process resulted in the transformation of initially gas-rich diffuse blue galaxies into gas-poor and red ones that form the dominant population now, the more extreme UDGs having undergone the process in a more distant past on average. Conclusions. The RPS in dense environments could be one of the major mechanisms for the formation of the large number of quiescent UDGs we observe in galaxy clusters.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".