Balancing the efficiency and stochasticity of star formation with dust extinction in <i>z</i> ≳ 10 galaxies observed by JWST
Bibliographic record
Abstract
ABSTRACT Early observations with JWST indicate an overabundance of bright galaxies at redshifts z ≳ 10 relative to Hubble-calibrated model predictions. More puzzling still is the apparent lack of evolution in the abundance of such objects between z ∼ 9 and the highest redshifts yet probed, z ∼ 13–17. In this study, we first show that, despite a poor match with JWST luminosity functions (LFs), semi-empirical models calibrated to rest-ultraviolet LFs and colours at 4 ≲ z ≲ 8 are largely consistent with constraints on the properties of individual JWST galaxies, including their stellar masses, ages, and spectral slopes. We then show that order-of-magnitude scatter in the star formation rate of galaxies (at fixed halo mass) can indeed boost the abundance of bright galaxies, provided that star formation is more efficient than expected in low-mass haloes. However, this solution to the abundance problem introduces tension elsewhere: because it relies on the upscattering of low-mass haloes into bright magnitude bins, one expects typical ages, masses, and spectral slopes to be much lower than constraints from galaxies observed thus far. This tension can be alleviated by non-negligible reddening, suggesting that – if the first batch of photometrically selected candidates are confirmed – star formation and dust production could be more efficient than expected in galaxies at z ≳ 10.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".