Color gradients of the galaxies at 0.5 < <i>z</i> < 1 I. Dependence on galaxy global properties
Bibliographic record
Abstract
Abstract We investigate the color gradients of galaxies at 0.5 < z < 1.0, using a sample of ∼35 000 galaxies with both spectroscopy from the final data release of the VIMOS Public Extragalactic Redshift Survey (VIPERS), and photometry in ultraviolet/optical/near-infrared bands from the VIPERS-Multi-Lambda Survey (VIPERS-MLS) and the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS). We estimate rest-frame colors, stellar mass, star formation rate from fitting the Spectral Energy Distribution (SED) for each galaxy, as well as a two-zone color Δ( u − r ), defined as the difference in rest-frame ( u − r ) color between the outer and inner region of the galaxy. We find that the two-zone color shows weak or no correlations with all galaxy properties considered except stellar mass. On average, Δ( u − r ) decreases with increasing stellar mass, indicating relatively red colors in galactic centers of more massive galaxies. We then compare the properties of “red-cored” and “blue-cored” galaxies, defined to have either a negative or a positive Δ( u − r ) respectively. Although the two types of galaxies show similar distributions in most properties, we find massive red-cored galaxies with M * > 10 10.5 M ⊙ to have larger sizes at given stellar mass (thus lower surface mass densities), and less massive red-cored galaxies with M * < 10 10.5 M ⊙ to have lower central galaxy fraction. These findings can be understood if one assumes that the star formation process happens from inside out, in the same way as recently emphasized in studies of low- z galaxies. The similarity between the galaxies at intermediate redshifts and those at low redshifts supports the idea that galaxy evolution since z ∼ 1 has been mainly driven by secular processes internal to galaxies rather than galaxy mergers or external environment.
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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 | 0.001 | 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) | 0.002 | 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".