EARLY-TYPE GALAXIES AT<i>z</i>∼ 1.3. II. MASSES AND AGES OF EARLY-TYPE GALAXIES IN DIFFERENT ENVIRONMENTS AND THEIR DEPENDENCE ON STELLAR POPULATION MODEL ASSUMPTIONS
Bibliographic record
Abstract
We have derived masses and ages for 79 early-type galaxies (ETGs) in different environments at z ∼ 1.3 in the Lynx supercluster and in the GOODS/CDF-S field using multi-wavelength (0.6–4.5 μm; KPNO, Palomar, Keck, Hubble Space Telescope , Spitzer ) data sets. At this redshift the contribution of the thermally pulsing asymptotic giant branch (TP-AGB) phase is important for ETGs, and the mass and age estimates depend on the choice of the stellar population model used in the spectral energy distribution fits. We describe in detail the differences among model predictions for a large range of galaxy ages, showing the dependence of these differences on age. Current models still yield large uncertainties. While recent models from Maraston and Charlot & Bruzual offer better modeling of the TP-AGB phase with respect to less recent Bruzual & Charlot models, their predictions do not often match. The modeling of this TP-AGB phase has a significant impact on the derived parameters for galaxies observed at high redshift. Some of our results do not depend on the choice of the model: for all models, the most massive galaxies are the oldest ones, independent of the environment. When using the Maraston and Charlot & Bruzual models, the mass distribution is similar in the clusters and in the groups, whereas in our field sample there is a deficit of massive ( M ≳ 10 11 M ☉ ) ETGs. According to those last models, ETGs belonging to the cluster environment host on average older stars with respect to group and field populations. This difference is less significant than the age difference in galaxies of different masses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".