Color Dispersion as an Indicator of Stellar Population Complexity: Insights from the Pixel Color–Magnitude Diagrams of 32 Bright Galaxies in Abell 1139 and Abell 2589
Bibliographic record
Abstract
Abstract We investigate the properties of bright galaxies of various morphological types in Abell 1139 and Abell 2589, using pixel color–magnitude diagram (pCMD) analysis. The sample contains 32 galaxies brighter than M r = −21.3 mag with spectroscopic redshifts, which are deeply imaged in the g and r bands using the MegaCam mounted on the Canada–France–Hawaii Telescope. After masking contaminants with two-step procedures, we examine how the detailed properties in the pCMDs depend on galaxy morphology and infrared color. The mean g − r color as a function of surface brightness (μ r ) in the pCMD of a galaxy shows good performance in distinguishing between early- and late-type galaxies, but it is not perfect because of the similarity between elliptical galaxies and bulge-dominated spiral galaxies. On the other hand, the g − r color dispersion as a function of μ r works better. We find that the best set of parameters for galaxy classification is a combination of the minimum color dispersion at μ r ≤ 21.2 mag arcsec−2 and the maximum color dispersion at 20.0 ≤ μ r ≤ 21.0 mag arcsec−2; the latter reflects the complexity of stellar populations at the disk component in a typical spiral galaxy. Finally, the color dispersion measurements of an elliptical galaxy appear to be correlated with the Wide-field Infrared Survey Explorer infrared color ([4.6]–[12]). This indicates that the complexity of stellar populations in an elliptical galaxy is related to its recent star formation activities. From this observational evidence, we infer that gas-rich minor mergers or gas interactions may have usually occurred during the recent growth of massive elliptical galaxies.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".