EPOCHS. II. The Ultraviolet Luminosity Function from 7.5 < z < 13.5 Using 180 arcmin<sup>2</sup> of Deep, Blank Fields from the PEARLS Survey and Public JWST Data
Bibliographic record
Abstract
Abstract We present an analysis of the ultraviolet luminosity function (UV LF) and star formation rate density of distant galaxies (7.5 < z < 13.5) in the “blank” fields of the Prime Extragalactic Areas for Reionization and Lensing Science (PEARLS) survey combined with Early Release Science data from the CEERS, GLASS, and NGDEEP surveys/fields and the first data release of JADES. We use strict quality cuts on EAZY photometric redshifts to obtain a reliable selection and characterization of high-redshift (z > 6.5) galaxies from a consistently processed set of deep, near-infrared imaging. Within an area of 180 arcmin2, we identify 1046 candidate galaxies at redshifts z > 6.5 and we use this sample to study the UV LF in four redshift bins between 7.5 < z < 13.5. The measured number density of galaxies at z = 8 and z = 9 matches those of past observations undertaken by the Hubble Space Telescope (HST). Our z = 10.5 measurements lie between early James Webb Space Telescope (JWST) results and past HST results, indicating cosmic variance may be the cause of previous high density measurements. However, the number densities of UV-luminous galaxies at z = 12.5 are high compared to predictions from simulations. When examining the star formation rate density of galaxies at this period, our observations are still largely consistent with a constant star formation efficiency, are slightly lower than previous early estimations using JWST, and support galaxy driven reionization at z ≤ 8.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".