A CLOSE-PAIR ANALYSIS OF DAMP MERGERS AT INTERMEDIATE REDSHIFTS
Bibliographic record
Abstract
We have studied the kinematics of ∼2800 candidate close-pair galaxies at 0.1 < z < 1.2 identified from the Canada–France–Hawaii Telescope Legacy Survey fields. Spectra of these systems were obtained using spectrometers on the 6.5 m Magellan and 5 m Hale telescopes. These data allow us to constrain the rate of dry mergers at intermediate redshifts and to test the "hot halo" model for quenching of star formation. Using virial radii estimated from the correlation between dynamical and stellar masses published by Leauthaud et al., we find that around 1/5 of our candidate pairs are likely to share a common dark matter halo (our metric for close physical association). These pairs are divided into red–red, blue–red, and blue–blue systems using the rest-frame colors classification method introduced in Chou et al.. Galaxies classified as red in our sample have very low star formation rates, but they need not be totally quiescent, and hence we refer to them as "damp," rather than "dry," systems. After correcting for known selection effects, the fraction of blue–blue pairs is significantly greater than that of red–red and blue–red pairs. Red–red pairs are almost entirely absent from our sample, suggesting that damp mergers are rare at z ∼ 0.5. Our data support models with a short merging timescale (<0.5 Gyr) in which star formation is enhanced in the early phase of mergers, but quenched in the late phase. Hot halo models may explain this behavior, but only if virial shocks that heat gas are inefficient until major mergers are nearly complete.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".