The ultraviolet and infrared star formation rates of compact group galaxies: an expanded sample
Bibliographic record
Abstract
Compact groups of galaxies provide insight into the role of low-mass, dense environments in galaxy evolution because the low velocity dispersions and close proximity of galaxy members result in frequent interactions that take place over extended time-scales. We expand the census of star formation in compact group galaxies by Tzanavaris et al. (2010) and collaborators with Swift UVOT, Spitzer IRAC and MIPS 24 μm photometry of a sample of 183 galaxies in 46 compact groups. After correcting luminosities for the contribution from old stellar populations, we estimate the dust-unobscured star formation rate (SFRUV) using the UVOT uvw2 photometry. Similarly, we use the MIPS 24 μm photometry to estimate the component of the SFR that is obscured by dust (SFRIR). We find that galaxies which are MIR-active (MIR-‘red’), also have bluer UV colours, higher specific SFRs, and tend to lie in H i-rich groups, while galaxies that are MIR-inactive (MIR-‘blue’) have redder UV colours, lower specific SFRs, and tend to lie in H i-poor groups. We find the SFRs to be continuously distributed with a peak at about 1 M⊙ yr−1, indicating this might be the most common value in compact groups. In contrast, the specific SFR distribution is bimodal, and there is a clear distinction between star-forming and quiescent galaxies. Overall, our results suggest that the specific SFR is the best tracer of gas depletion and galaxy evolution in compact groups.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".