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 arcmin 2 . Based on our initial reduction of the first half of this survey, we reach 5 σ depths up to mag = 29.5–29.9 between 1 and 5 μ m. 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>z</mml:mi> <mml:mo>=</mml:mo> <mml:msubsup> <mml:mrow> <mml:mn>15.6</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>0.3</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>0.4</mml:mn> </mml:mrow> </mml:msubsup> </mml:math> , which has a blue <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>β</mml:mi> <mml:mo>=</mml:mo> <mml:mo>−</mml:mo> <mml:msubsup> <mml:mrow> <mml:mn>3.02</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>0.46</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>0.42</mml:mn> </mml:mrow> </mml:msubsup> </mml:math> and a very low inferred stellar mass of M * = 10 7.4 M ⊙ . We also discover a series of faint, low-mass dwarf galaxies with M * < 10 8.5 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".