An enhanced merger fraction within the galaxy population of the SSA22 protocluster at<i>z</i> = 3.1
Bibliographic record
Abstract
The overdense environments of protoclusters of galaxies in the early Universe (z > 2) are expected to accelerate the evolution of galaxies, with an increased rate of stellar mass assembly and black hole accretion compared to co-eval galaxies in the average density ‘field’. These galaxies are destined to form the passive population of massive galaxies that dominate the cores of rich clusters today. While signatures of the accelerated growth of galaxies in the SSA22 protocluster (z = 3.1) have been observed, the mechanism driving this remains unclear. Here we show an enhanced rate of galaxy–galaxy mergers could be responsible. We morphologically classify Lyman-break Galaxies (LBGs) in the SSA22 protocluster and compare these to those of galaxies in the field at z = 3.1 as either active mergers or non-merging using Hubble Space Telescope ACS/F814W imaging, probing the rest-frame ultraviolet stellar light. We measure a merger fraction of 48 ± 10 per cent for LBGs in the protocluster compared to 30 ± 6 per cent in the field. Although the excess is marginal, an enhanced rate of mergers in SSA22 hints that galaxy–galaxy mergers are one of the key channels driving accelerated star formation and AGN growth in protocluster environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".