The Gemini Deep Deep Survey. VIII. When Did Early‐Type Galaxies Form?
Bibliographic record
Abstract
We have used the Hubble Space Telescope 's Advanced Camera for Surveys (Ford et al. 2003) to measure the cumulative mass density in morphologically selected early-type galaxies over the redshift range 0.8 < z < 1.7. Our imaging data set covers four well-separated sight lines and is roughly intermediate (in terms of both depth and area) between the GOODS/GEMS imaging data and the images obtained in the Hubble Deep Field campaigns. Our images contain 144 galaxies with ultradeep spectroscopy obtained as part of the Gemini Deep Deep Survey. These images have been analyzed using a new purpose-written morphological analysis code, which improves the reliability of morphological classifications by adopting a "quasi-Petrosian" image thresholding technique. We find that at z ~ 1 about 80% of the stars living in the most massive galaxies reside in early-type systems. This fraction is similar to that seen in the local universe. However, we detect very rapid evolution in this fraction over the range 0.8 < z < 1.7, suggesting that over this redshift range the strong morphology-mass relationship seen in the nearby universe is beginning to fall into place. By comparing our images to published spectroscopic classifications, we show that little ambiguity exists in connecting spectral classes to morphological classes for spectroscopically quiescent systems. However, the mass density function of early-type galaxies is evolving more rapidly than that of spectroscopically quiescent systems, which we take as further evidence that we are witnessing the formation of massive early-type galaxies over the 0.8 < z < 1.7 redshift range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".