The first large catalogue of spectroscopic redshifts in Webb’s first deep field, SMACS J0723.3−7327
Bibliographic record
Abstract
ABSTRACT We present a spectroscopic redshift catalogue of the SMACS J0723.3−7327 field (‘Webb’s First Deep Field’) obtained from JWST/NIRISS grism spectroscopy and supplemented with JWST/NIRSpec and VLT/MUSE redshifts. The catalogue contains a total of 190 sources with secure spectroscopic redshifts, including 156 NIRISS grism redshifts, 123 of which are for sources whose redshifts were previously unknown. These new grism redshifts are secured with two or more spectroscopic features (64 sources), or with a single spectral feature whose identity is secured from the object’s nine-band photometric redshift (59 sources). These are complemented with 17 NIRSpec and 48 MUSE redshifts, including six new NIRSpec redshifts identified in this work. In addition to the zcl = 0.39 cluster galaxy redshifts (for which we provide ∼40 new NIRISS absorption-line redshifts), we also find three prominent galaxy overdensities at higher redshifts – at z = 1.1, z = 1.4, and z = 2.0 – that were until now not seen in the JWST/NIRSpec and VLT/MUSE data. The paper describes the characteristics of our spectroscopic redshift sample and the methodology we have employed to obtain it. Our redshift catalogue is made available to the community at https://niriss.github.io/smacs0723.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.018 |
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".