The <i>z</i> ≳ 9 Galaxy UV Luminosity Function from the JWST Advanced Deep Extragalactic Survey: Insights into Early Galaxy Evolution and Reionization
Bibliographic record
Abstract
Abstract The high-redshift UV luminosity function provides important insights into the evolution of early galaxies. JWST has revealed an unexpectedly large population of bright ( M UV ≲ −20) galaxies at z ≳ 10, implying fundamental changes in the star-forming properties of galaxies at increasingly early times. However, constraining the fainter population ( M UV ≳ −18) has been more challenging. In this work, we present the z ≳ 9 UV luminosity function from the JWST Advanced Deep Extragalactic Survey. We calculate the UV luminosity function from several hundred z ≳ 9 galaxy candidates that reach UV luminosities of M UV ∼ −17 in redshift bins of z ∼ 8.5–12 (309 candidates) and z ∼ 12–16 (63 candidates). We search for candidates at z ∼ 16–22.5 and find none. We also estimate the z ∼ 14–16 luminosity function from the z ≥ 14 subset of the z ∼ 12–16 sample. Consistent with other measurements, we find an excess of bright galaxies that is in tension with many theoretical models, especially at z ≳ 12. However, we also find high number densities at −18 ≲ M UV ≲ −17, suggesting that there is a larger population of faint galaxies than expected, as well as bright ones. From our parametric fits for the luminosity function, we find steep faint-end slopes of −2.5 ≲ α ≲ −2.3, suggesting a large population of faint ( M UV ≳ −17) galaxies. Combined, the high normalization and steep faint-end slope of the luminosity function could imply that the reionization process is appreciably underway as early as 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".