VERTICO. VII. Environmental Quenching Caused by the Suppression of Molecular Gas Content and Star Formation Efficiency in Virgo Cluster Galaxies
Bibliographic record
Abstract
Abstract We study how environment regulates the star formation cycle of 33 Virgo Cluster satellite galaxies on 720 pc scales. We present the resolved star-forming main sequence for cluster galaxies, dividing the sample based on their global H i properties and comparing to a control sample of field galaxies. H i –poor cluster galaxies have reduced star formation rate (SFR) surface densities with respect to both H i –normal cluster and field galaxies (∼0.5 dex), suggesting that mechanisms regulating the global H i content are responsible for quenching local star formation. We demonstrate that the observed quenching in H i –poor galaxies is caused by environmental processes such as ram pressure stripping (RPS), simultaneously reducing the molecular gas surface density and star formation efficiency (SFE) compared to regions in H i –normal systems (by 0.38 and 0.22 dex, respectively). We observe systematically elevated SFRs that are driven by increased molecular gas surface densities at fixed stellar mass surface density in the outskirts of early stage RPS galaxies, while SFE remains unchanged with respect to the field sample. We quantify how RPS and starvation affect the star formation cycle of inner and outer galaxy disks as they are processed by the cluster. We show both are effective quenching mechanisms, with the key difference being that RPS acts upon the galaxy outskirts while starvation regulates the star formation cycle throughout disk, including within the truncation radius. For both processes, the quenching is caused by a simultaneous reduction in the molecular gas surface densities and SFE at fixed stellar mass surface density.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".