The rate and contribution of mergers to mass assembly from NIRCam observations of galaxy candidates up to 13.3 billion years ago
Bibliographic record
Abstract
ABSTRACT We present an analysis of the galaxy merger rate in the redshift range $4.0\lt z\lt 9.0$ (i.e. about 1.5 to 0.5 Gyr after the big bang) based on visually identified galaxy mergers from morphological parameter analysis. Our data set is based on high-resolution NIRCam JWST data (a combination of F150W and F2000W broad-band filters) in the low-to-moderate magnification ($\mu \lt 2$) regions of the Abell 2744 cluster field. From a parent set of 675 galaxies $(M_{U}\in [-26.6,-17.9])$, we identify 64 merger candidates from the Gini, $M_{20}$ and asymmetry morphological parameters, leading to a merger fraction $f_m=0.11\pm 0.04$. There is no evidence of redshift evolution of $f_m$ even at the highest redshift considered, thus extending well into the epoch of reionization the constant trend seen previously at $z\lesssim 6$. Furthermore, we investigate any potential redshift-dependent differences in the specific star formation rates between mergers and non-mergers. Our analysis reveals no significant correlation in this regard, with deviations in the studied redshift range typically falling within $(1-1.5)\sigma$ from the null hypothesis that can be attributed to sample variance and measurement errors. Finally, we also demonstrate that the classification of a merging system is robust with respect to the observed (and equivalently rest frame) wavelength of the high-quality JWST broad-band images used. This preliminary study highlights the potential for progress in quantifying galaxy assembly through mergers during the epoch of reionization, with significant sample size growth expected from upcoming large JWST infrared imaging data sets.
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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 | 0.002 |
| 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 | 0.000 |
| 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".