Bursty star formation and galaxy–galaxy interactions in low-mass galaxies 1 Gyr after the Big Bang
Bibliographic record
Abstract
ABSTRACT We use CANUCS JWST/NIRCam imaging of galaxies behind the gravitationally-lensing cluster MACS J0417.5-1154 to investigate star formation burstiness in low-mass (M⋆ ∼ 108 M⊙) galaxies at z ∼ 4.7–6.5. Our sample of 123 galaxies is selected using the Lyman break selection and photometric emission-line excess methods. Sixty per cent of the 123 galaxies in this sample have Hα-to-UV flux ratios that deviate significantly from the range of Hα-to-UV ratio values consistent with smooth and steady star formation histories. This large fraction indicates that the majority of low-mass galaxies is experiencing bursty star formation histories at high redshift. We also searched for interacting galaxies in our sample and found that they are remarkably common ($\sim 40~{{\ \rm per\ cent}}$ of the sample). Compared to non-interacting galaxies, interacting galaxies are more likely to have very low Hα-to-UV ratios, suggesting that galaxy–galaxy interactions enhance star formation burstiness and enable faster quenching (with time-scales of ≲100 Myr) that follows the rapid rise of star formation activity. Given the high frequency of galaxy–galaxy interactions and the rapid SFR fluctuations they appear to cause, we conclude that galaxy–galaxy interactions could be a leading cause of bursty star formation in low-mass, high-z galaxies. They could thus play a significant role in the evolution of the galaxy population at early cosmological times.
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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".