The impact of UV variability on the abundance of bright galaxies at <i>z</i> ≥ 9
Bibliographic record
Abstract
ABSTRACT JWST observations have revealed a population of galaxies bright enough that potentially challenge standard galaxy formation models in the Λ cold dark matter (ΛCDM) cosmology. Using a minimal empirical framework, we investigate the influence of variability on the rest-frame ultra-violet (UV) luminosity function of galaxies at z ≥ 9. Our study differentiates between the median UV radiation yield and the variability of UV luminosities of galaxies at a fixed dark matter halo mass. We primarily focus on the latter effect, which depends on halo assembly and galaxy formation processes and can significantly increase the abundance of UV-bright galaxies due to the upscatter of galaxies in lower-mass haloes. We find that a relatively low level of variability, σUV ≈ 0.75 mag, matches the observational constraints at z ≈ 9. However, increasingly larger σUV is necessary when moving to higher redshifts, reaching $\sigma _{\rm UV} \approx 2.0\, (2.5)\, {\rm mag}$ at z ≈ 12 (16). This implied variability is consistent with expectations of physical processes in high-redshift galaxies such as bursty star formation and dust clearance during strong feedback cycles. Photometric constraints from JWST at z ≳ 9 therefore can be reconciled with a standard ΛCDM-based galaxy formation model calibrated at lower redshifts without the need for adjustments to the median UV radiation yield.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".