Evidence for the Evolution of Young Early-Type Galaxies in the GOODS/CDF-S Field
Bibliographic record
Abstract
We have developed an efficient photometric technique for identifying young early-type galaxy candidates using a combination of photometric redshifts, spectral-type classification, and optical/near-infrared colors. Applying our technique to the GOODS HST ACS and VLT ISAAC data, we have selected a complete and homogeneous sample of young elliptical candidates among early-type field galaxies. The distribution of structural parameters for these candidates shows that their selection, which is based on early spectral types, is fully consistent with early morphological types. We investigate the evolution of their luminosities and colors as a function of redshift and galaxy mass and find evidence for an increasing starburst mass fraction in these young early-type galaxy candidates at higher redshifts, which we interpret in terms of massive field galaxies experiencing more massive/intense starbursts at higher redshifts. Moreover, we find indications for a systematically larger young elliptical fraction among sub- L */2 early-type galaxies compared to their brighter counterparts. The total fraction among the field early-type galaxies increases with redshift, irrespective of galaxy luminosity. Our results are most consistent with galaxy formation scenarios in which stars in massive early-type field galaxies are assembled earlier than in their low-mass counterparts.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".