Frequency and properties of bars in cluster and field galaxies at intermediate redshifts
Bibliographic record
Abstract
We present a study of large-scale bars in field and cluster environments out to redshifts of ~0.8 using a final sample of 945 moderately inclined disk galaxies drawn from the EDisCS project. We characterize bars and their host galaxies and look for relations between the presence of a bar and the properties of the underlying disk. We investigate whether the fraction and properties of bars in clusters are different from their counterparts in the field. The properties of bars and disks are determined by ellipse fits to the surface brightness distribution of the galaxies using HST/ACS images in the F814W filter. The bar identification is based on quantitative criteria after highly inclined (> 60°) systems have been excluded. The total optical bar fraction in the redshift range (median ), averaged over the entire sample, is 25% (20% for strong bars). For the cluster and field subsamples, we measure bar fractions of 24% and 29%, respectively. We find that bars in clusters are on average longer than in the field and preferentially found close to the cluster center, where the bar fraction is somewhat higher (~31%) than at larger distances (~18%). These findings however rely on a relatively small subsample and might be affected by small number statistics. In agreement with local studies, we find that disk-dominated galaxies have a higher optical bar fraction (~45%) than bulge-dominated galaxies (~15%). This result is based on Hubble types and effective radii and does not change with redshift. The latter finding implies that bar formation or dissolution is strongly connected to the emergence of the morphological structure of a disk and is typically accompanied by a transition in the Hubble type. The question whether internal or external factors are more important for bar formation and evolution cannot be answered definitely. On the one hand, the bar fraction and properties of cluster and field samples of disk galaxies are quite similar, indicating that internal processes are crucial for bar formation. On the other hand, we find evidence that cluster centers are favorable locations for bars, which suggests that the internal processes responsible for bar growth are supported by the typical interactions taking place in such environments.
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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 | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".