JEMS: A Deep Medium-band Imaging Survey in the Hubble Ultra Deep Field with JWST NIRCam and NIRISS
Bibliographic record
Abstract
Abstract We present JWST Extragalactic Medium-band Survey, the first public medium-band imaging survey carried out using JWST/NIRCam and NIRISS. These observations use ∼2 and ∼4 μ m medium-band filters (NIRCam F182M, F210M, F430M, F460M, F480M; and NIRISS F430M and F480M in parallel) over 15.6 arcmin 2 in the Hubble Ultra Deep Field (UDF), thereby building on the deepest multiwavelength public data sets available anywhere on the sky. We describe our science goals, survey design, NIRCam and NIRISS image reduction methods, and describe our first data release of the science-ready mosaics, which reach 5 σ point-source limits (AB mag) of ∼29.3–29.4 in 2 μ m filters and ∼28.2–28.7 at 4 μ m. Our chosen filters create a JWST imaging survey in the UDF that enables novel analysis of a range of spectral features potentially across the redshift range of 0.3 < z < 20, including Paschen- α , H α +[N ii ], and [O iii ]+H β emission at high spatial resolution. We find that our JWST medium-band imaging efficiently identifies strong line emitters (medium-band colors >1 mag) across redshifts 1.5 < z < 9.3, most prominently H α +[N ii ] and [O iii ]+H β . We present our first data release including science-ready mosaics of each medium-band image available to the community, adding to the legacy value of past and future surveys in the UDF. This survey demonstrates the power of medium-band imaging with JWST, informing future extragalactic survey strategies using JWST observations.
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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 | 0.001 | 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 | 0.001 | 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".