THE CFHTLS-DEEP CATALOG OF INTERACTING GALAXIES. I. MERGER RATE EVOLUTION TO<i>z</i>= 1.2
Bibliographic record
Abstract
We present the rest-frame optical galaxy merger fraction between 0.2 < z < 1.2, as a function of stellar mass and optical luminosity, as observed by the Canada–France–Hawaii Telescope Legacy Deep Survey (CFHTLS-Deep). We developed a new classification scheme to identify major galaxy–galaxy mergers based on the presence of tidal tails and bridges. These morphological features are signposts of recent and ongoing merger activity. Through the visual classification of all galaxies, down to i vega ⩽ 22.2 (≈27,000 galaxies) over 2 square degrees, we have compiled the CFHTLS-Deep Catalog of Interacting Galaxies, with ≈ 1600 merging galaxies. We find the merger fraction to be 4.3% ± 0.3% at z ∼ 0.3 and 19.0% ± 2.5% at z ∼ 1, implying evolution of the merger fraction going as (1 + z ) m , with m = 2.25 ± 0.24. This result is inconsistent with a mild or non-evolving ( m < 1.5) scenario at a ≳4σ level of confidence. A mild trend, where by massive galaxies with M * >10 10.7 M ☉ are undergoing fewer mergers than less massive systems ( M * ∼ 10 10 M ☉ ), consistent with the expectations of galaxy assembly downsizing is observed. Our results also show that interacting galaxies have on average SFRs double that found in non-interacting field galaxies. We conclude that (1) the optical galaxy merger fraction does evolve with redshift, (2) the merger fraction depends mildly on stellar mass, with lower mass galaxies having higher merger fractions at z < 1, and (3) star formation is triggered at all phases of a merger, with larger enhancements at later stages, consistent with N -body simulations.
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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 | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".