HISTORY OF GALAXY INTERACTIONS AND THEIR IMPACT ON STAR FORMATION OVER THE LAST 7 Gyr FROM GEMS
Bibliographic record
Abstract
Accepted by the Astrophysical Journal We perform a comprehensive estimate of the frequency of galaxy mergers and their impact on star formation over z ∼ 0.24–0.80 (lookback time Tb ∼ 3–7 Gyr) using ∼ 3600 (M ≥ 1 × 10 9 M⊙) galaxies with GEMS HST, COMBO-17, and Spitzer data. Our results are: (1) Among ∼ 790 high mass (M ≥ 2.5 × 10 10 M⊙) galaxies, the visually-based merger fraction over z ∼ 0.24–0.80, ranges from 9% ± 5 % to 8 % ± 2%. Lower limits on the major merger and minor merger fraction over this interval range from 1.1 % to 3.5 % , and 3.6 % to 7.5%, respectively. This is the first, albeit approximate, empirical estimate of the frequency of minor mergers over the last 7 Gyr. Assuming a visibility timescale of ∼ 0.5 Gyr, it follows that over Tb ∼ 3–7 Gyr, ∼ 68 % of high mass systems have undergone a merger of mass ratio> 1/10, with ∼ 16%, 45%, and 7 % of these corresponding respectively to major, minor, and ambiguous ‘major or minor ’ mergers. The average merger rate is ∼ a few ×10 −4 galaxies Gyr −1 Mpc −3. Among ∼ 2840 blue cloud galaxies of mass M ≥ 1.0 ×10 9 M⊙, similar results hold. (2) We compare the empirical merger fraction and merger rate for high mass galaxies to three ΛCDM-based models: halo occupation distribution models, semi-analytic models, and hydrodynamic SPH simulations. We find
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".