A Large Population of Faint 8 < z < 16 Galaxies Found in the First JWST NIRCam Observations of the NGDEEP Survey
Bibliographic record
Abstract
Abstract We present an early analysis on the search for high-redshift galaxies using the deepest public JWST imaging to date, the NGDEEP field. These data consist of six-band NIRCam imaging on the Hubble Ultra Deep Field Parallel 2 (HUDF-Par2), covering a total area of 6.3 arcmin2. Based on our initial reduction of the first half of this survey, we reach 5σ depths up to mag = 29.5–29.9 between 1 and 5 μm. Such depths present an unprecedented opportunity to begin exploring the very early universe with JWST. As such, we find high-redshift galaxies by examining the spectral energy distribution of all F444W detections and present 16 new z > 8.5 galaxies identified using two different photometric redshift codes: LePhare and EAZY combined with other significance criteria. The highest-redshift object in our sample is at z = 15.6 − 0.3 + 0.4 , which has a blue β = − 3.02 − 0.46 + 0.42 and a very low inferred stellar mass of M * = 107.4 M ⊙. We also discover a series of faint, low-mass dwarf galaxies with M * < 108.5 M ⊙ at z ∼ 9 that have blue colors, flat surface brightness profiles, and small sizes <1 kpc. Comparing to previous work in the HUDF-Par2, we find 21 6 < z < 9 candidates including two z = 8 major mergers. One of these merger candidates has an additional two z = 8 sources within 30″, indicating that it may form part of an overdensity. We also compare our results to theory, finding no significant disagreement with a few cold-dark-matter-based models. The discovery of these objects demonstrates the critical need for deeper, or similar depth but wider-area, JWST surveys to explore the early universe.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".