Compact groups in theory and practice - II. Comparing the observed and predicted nature of galaxies in compact groups
Bibliographic record
Abstract
We examine the properties of galaxies in compact groups (CGs) identified in a mock galaxy catalogue based upon the Millennium Run simulation. The overall properties of groups identified in projection are in general agreement with the best available observational constraints. However, only ∼30 per cent of these simulated groups are found to be truly compact in three dimensions, suggesting that interlopers strongly affect our observed understanding of the properties of galaxies in CGs. These simulations predict that genuine CG galaxies are an extremely homogeneous population, confined nearly exclusively to the red sequence: they are best described as ‘red and dead’ ellipticals. When interlopers are included, the population becomes much more heterogeneous, due to bluer, star-forming, gas-rich, late-type galaxies incorrectly identified as CG members. These models suggest that selection of members by redshift, such that the line-of-sight velocity dispersion of the group is less than 1000 km s−1, significantly reduces contamination to the 30 per cent level. Selection of members by galaxy colour, a technique used frequently for galaxy clusters, is also predicted to dramatically reduce contamination rates for CG studies.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".