The GLASS-JWST Early Release Science programme: The NIRISS spectroscopic catalogue
Bibliographic record
Abstract
We present a spectroscopic redshift catalogue of sources within the Abell 2744 cluster field, derived from JWST/NIRISS observations, obtained as part of the GLASS-JWST Early Release Science programme. We describe the data reduction, the contamination modelling, and the source detection, as well as the data quality assessment, the redshift determination, and the validation. The catalogue consists of 354 secure and 134 tentative redshifts, of which 245 are new spectroscopic redshifts, spanning the range 0.1≤ z ≤8.2. These include 17 galaxies at the cluster redshift, one galaxy at z ≈8, and a triply imaged galaxy at z = 2.653±0.002. Comparing against galaxies with existing spectroscopic redshifts ( z spec ), we find a small offset of Δ z =( z spec − z NIRISS )/(1+ z spec ) =(1.3±1.6)×10 −3 . We also present a forced extraction tool ( PYGRIFE ) and a visualisation tool ( PYGCG ) to the community, to aid with the reduction and classification of grism data. This catalogue will enable future studies of the spatially resolved properties of galaxies throughout cosmic noon, including dust attenuation and star formation. As a first application of the catalogue, we discuss the spectroscopic confirmation of multiple image systems and the identification of multiple overdensities at 1< z <2.7.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.021 |
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".