The role of molecular gas in galaxy transition in compact groups
Bibliographic record
Abstract
Compact groups (CGs) provide an environment in which interactions between galaxies and with the intra-group medium enable and accelerate galaxy transitions from actively star forming to quiescent. Galaxies in transition from active to quiescent can be selected, by their infrared (IR) colors, as canyon or infrared transition zone (IRTZ) galaxies. We used a sample of CG galaxies with IR data from the Wide Field Infrared Survey Explorer (WISE) allowing us to calculate the stellar mass and star formation rate (SFR) for each galaxy. Furthermore, we present new CO(1−0) data for 27 galaxies and collect data from the literature to calculate the molecular gas mass for a total sample of 130 galaxies. This data set allows us to study the difference in the molecular gas fraction (Mmol/M∗) and star formation efficiency (SFE = SFR/Mmol) between active, quiescent, and transitioning (i.e., canyon and IRTZ) galaxies. We find that transitioning galaxies have a mean molecular gas fraction and a mean SFE that are significantly lower than those of actively star-forming galaxies. The molecular gas fraction is higher than that of quiescent galaxies, whereas the SFE is similar. These results indicate that the transition from actively star-forming to quiescent in CG galaxies goes along with a loss of molecular gas, possibly due to tidal forces exerted from the neighboring galaxies or a decrease in the gas density. In addition, the remaining molecular gas loses its ability to form stars efficiently, possibly owing to turbulence perturbing the gas,as seen in other, well-studied examples such as Stephan’s Quintet and HCG 57. Thus, the amount and properties of molecular gas play a crucial role in the environmentally driven transition of galaxies from actively star forming to quiescent.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".