WHERE DO WET, DRY, AND MIXED GALAXY MERGERS OCCUR? A STUDY OF THE ENVIRONMENTS OF CLOSE GALAXY PAIRS IN THE DEEP2 GALAXY REDSHIFT SURVEY
Bibliographic record
Abstract
We study the environment of wet, dry, and mixed galaxy mergers at 0.75 < z < 1.2 using close pairs in the DEEP2 Galaxy Redshift Survey. We find that the typical environment of mixed and dry merger candidates is denser than that of wet mergers, mostly due to the color-density relation. While the galaxy companion rate (Nc) is observed to increase with overdensity, using N-body simulations we find that the fraction of pairs that will eventually merge decreases with the local density, predominantly because interlopers are more common in dense environments. After taking into account the merger probability of pairs as a function of local density, we find only marginal environment dependence of the fractional merger rate for wet mergers over the redshift range we have probed. On the other hand, the fractional dry merger rate increases rapidly with local density due to the increased population of red galaxies in dense environments. We also find that the environment distribution of K+A galaxies is similar to that of wet mergers alone and of wet+mixed mergers, suggesting a possible connection between K+A galaxies and wet and/or wet+mixed mergers. We conclude that, as early as z ~ 1, high-density regions are the preferred environment in which dry mergers occur, and that present-day red-sequence galaxies in overdense environments have, on average, undergone 1.2+-0.3 dry mergers since this time, accounting for (38+-10)% of their mass accretion in the last 8 billion years. Our findings suggest that dry mergers are crucial in the mass-assembly of massive red galaxies in dense environments, such as Brightest Cluster Galaxies (BCGs) in galaxy groups and clusters.
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 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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".