The Evolution of Galaxy Mergers and Morphology at<i>z</i>< 1.2 in the Extended Groth Strip
Bibliographic record
Abstract
We present the quantitative rest-frame B morphological evolution and galaxy merger fraction at 0.2 < z < 1.2 as observed by the All-Wavelength Extended Groth Strip International Survey (AEGIS). We use the Gini coefficient and M 20 to identify major mergers and classify galaxy morphology for a volume-limited sample of 3009 galaxies brighter than 0.4 L * B , assuming pure luminosity evolution. We find that the merger fraction remains roughly constant at 10% ± 2% for 0.2 < z < 1.2. The fraction of E/S0/Sa galaxies increases from 21% ± 3% at z ∼ 1.1 to 44% ± 9% at z ∼ 0.3, while the fraction of Sb-Ir galaxies decreases from 64% ± 6% at z ∼ 1.1 to 47% ± 9% at z ∼ 0.3. The majority of z < 1.2 Spitzer MIPS 24 μm sources with L (IR) > 10 11 L ☉ are disk galaxies, and only ~15% are classified as major merger candidates. Edge-on and dusty disk galaxies (Sb-Ir) are almost a third of the red sequence at z ∼ 1.1, while E/S0/Sa make up over 90% of the red sequence at z ∼ 0.3. Approximately 2% of our full sample are red mergers. We conclude (1) the merger rate does not evolve strongly between 0.2 < z < 1.2; (2) the decrease in the volume-averaged star formation rate density since z ∼ 1 is a result of declining star formation in disk galaxies rather than a disappearing population of major mergers; (3) the build-up of the red sequence at z < 1 can be explained by a doubling in the number of spheroidal galaxies since z ∼ 1.2.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".