Luminosity functions of cluster galaxies
Bibliographic record
Abstract
We derive NUV luminosity functions for 6471 NUV detected galaxies in 28 0.02 < z < 0.08 clusters and consider their dependence on cluster properties. We consider optically red and blue galaxies and explore how their NUV LFs vary in several cluster subsamples, selected to best show the influence of environment. Our composite LF is well fit by the Schechter form with M*NUV = −18.98 ± 0.07 and α = −1.87 ± 0.03 in good agreement with values for the Coma centre and the Shapley supercluster, but with a steeper slope and brighter L* than in Virgo. The steep slope is due to the contribution of massive quiescent galaxies that are faint in the NUV. There are significant differences in the NUV LFs for clusters having low and high X-ray luminosities and for sparse and dense clusters, though none are particularly well fitted by the Schechter form, making a physical interpretation of the parameters difficult. When splitting clusters into two subsamples by X-ray luminosity, the ratio of low to high NUV luminosity galaxies is higher in the high X-ray luminosity subsample (i.e., the luminosity function is steeper across the sampled luminosity range). In subsamples split by surface density, when characterised by Schechter functions the dense clusters have an M* about a magnitude fainter than that of the sparse clusters and α is steeper (−1.9 vs. −1.6, respectively). The differences in the data appear to be driven by changes in the LF of blue (star-forming) galaxies. This appears to be related to interactions with the cluster gas. For the blue galaxies alone, the luminosity distributions indicate that for high LX and high velocity dispersion cluster subsamples (i.e., the higher mass clusters), there are relatively fewer high UV luminosity galaxies (or correspondingly a relative excess of low UV luminosity galaxies) in comparison the lower mass cluster subsamples.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".