Galaxy mergers in the epoch of reionization – I. A JWST study of pair fractions, merger rates, and stellar mass accretion rates at <i>z</i> = 4.5–11.5
Bibliographic record
Abstract
ABSTRACT We present a full analysis of galaxy major merger pair fractions, merger rates, and mass accretion rates, thus uncovering the role of mergers in galaxy formation at the earliest previously unexplored epoch of $4.5< z< 11.5$. We target galaxies with masses $\log _{10}(\mathrm{\it M}_*/\mathrm{M}_\odot) = 8.0 {\!-\!} 10.0$, utilizing data from eight JWST Cycle-1 fields [CEERS (Cosmic Evolution Early Release Science Survey), JADES (JWST Advanced Deep Extragalactic Survey) GOODS-S, NEP-TDF (North Ecliptic Pole Time-Domain Field), NGDEEP (Next-Generation Deep Extragalactic Exploratory Public Survey), GLASS (Grism Lens Amplified Survey from Space), El-Gordo, SMACS-0723, MACS-0416], covering an unmasked area of 189.36 $\mathrm{arcmin}^2$. We develop a new probabilistic pair-counting methodology that integrates full photometric redshift posteriors and corrects for detection incompleteness to quantify close pairs with physical projected separations between 20 and 50 kpc. Our analysis reveals an increase in pair fractions up to $z = 8$, reaching $0.211 \pm 0.065$, followed by a statistically flat evolution to $z = 11.5$. We find that the galaxy merger rate increases from the local Universe up to $z = 6$ and then stabilizes at a value of $\sim 6$ Gyr$^{-1}$ up to $z = 11.5$. The redshift evolution of both pair fractions and merger rates is well described by a power-law plus exponential model. In addition, we measure that the average galaxy increases its stellar mass due to mergers by a factor of $2.77 \pm 0.99$ from redshift $z = 10.5$ to $z = 5.0$. Lastly, we investigate the impact of mergers on galaxy stellar mass growth, revealing that mergers contribute as much as $71 \pm 25~{{\ \rm per\ cent}}$ to galaxy stellar mass growth. This indicates that mergers drive about half of galaxy assembly at high redshift.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".