JADES Initial Data Release for the Hubble Ultra Deep Field: Revealing the Faint Infrared Sky with Deep JWST NIRCam Imaging
Bibliographic record
Abstract
Abstract JWST has revolutionized the field of extragalactic astronomy with its sensitive and high-resolution infrared view of the distant Universe. Adding to the new legacy of JWST observations, we present the first NIRCam imaging data release from the JWST Advanced Deep Extragalactic Survey (JADES), providing nine filters of infrared imaging of ∼25 arcmin 2 covering the Hubble Ultra Deep Field and portions of Great Observatories Origins Deep Survey South. Utilizing 87 on-sky dual-filter hours of exposure time, these images reveal the deepest ever near-infrared view of this iconic field. We supply carefully constructed nine-band mosaics of the JADES bands, as well as matching reductions of five additional bands from the JWST Extragalactic Medium-band Survey. Combining with existing Hubble Space Telescope imaging, we provide 23-band space-based photometric catalogs and photometric redshifts for ≈47,500 sources. To promote broad engagement with JADES, we have created an interactive FitsMap website to provide an interface for professional researchers and the public to experience these JWST data sets. Combined with the first JADES NIRSpec data release, these public JADES imaging and spectroscopic data sets provide a new foundation for discoveries of the infrared Universe by the worldwide scientific community.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".