Constraining the major merger history of <i>z</i> ∼ 3–9 galaxies using JADES: dominant <i>in situ</i> star formation
Bibliographic record
Abstract
ABSTRACT We present a comprehensive analysis of galaxy close-pair fractions and major merger rates to evaluate the importance of mergers in the hierarchical growth of galaxies over cosmic time. This study focuses on the previously poorly understood redshift range of $z \approx 3\!-\!9$ using JADES observations. Our mass-complete sample includes primary galaxies with stellar masses of ${\rm log}(M_\star /{\rm M_\odot }) = [8, 10]$, having major companions (mass ratio $\ge 1/4$) selected by $5\!-\!30$ pkpc projected separation and redshift proximity criteria. Pair fractions are measured using a statistically robust method incorporating photometric redshift posteriors and available spectroscopic data. The pair fraction evolves with redshift and shows dependence on the stellar mass: at ${\rm log}(M_\star /{\rm M_\odot }) = [8.0, 8.5]$ there is an increase up to $z\sim 5\!-\!6$, followed by a turnover, while at higher stellar masses there is a flattening and weak decline with increasing redshift. Similarly, the derived galaxy major merger rate increases and flattens beyond $z \sim 6$ to $2\!-\!8~{\rm Gyr^{-1}}$ per galaxy, showing a weak scaling with stellar mass, driven by the evolution of the galaxy stellar mass function. A comparison between the cumulative mass accretion from major mergers and the mass assembled through star formation indicates that major mergers contribute approximately $3\!-\!13~{{\ \rm per\ cent}}$ to the total mass growth over the studied redshift range, which is in agreement with the ex situ mass fraction estimated from our simple numerical model. These results highlight that major mergers contribute little to the direct stellar mass growth compared to in situ star formation but could still play an indirect role by driving star formation itself.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".