Early Results from GLASS-JWST. VI. Extreme Rest-optical Equivalent Widths Detected in NIRISS Wide Field Slitless Spectroscopy
Bibliographic record
Abstract
Abstract Wide Field Slitless Spectroscopy (WFSS) provides a powerful tool for detecting strong line emission in star-forming galaxies (SFGs) without the need for target preselection. As part of the GLASS-JWST Early Release Science (ERS) program, we leverage the near-infrared wavelength capabilities of NIRISS (1–2.2 μ m) to observe rest-optical emission lines out to z ∼ 3.4, to a depth and with a spatial resolution higher than ever before (H α to z < 2.4; [O iii ]+H β to z < 3.4). In this Letter we constrain the rest-frame [O III ] λ 5007 equivalent width (EW) distribution for a sample of 76 1 < z < 3.4 SFGs in the A2744 Hubble Frontier Field and determine an abundance fraction of extreme emission line galaxies with EW > 750Å in our sample to be 12%. We determine a strong correlation between the measured H β and [O III ] λ 5007 EWs, supporting that the high [O III ] λ 5007 EW objects require massive stars in young stellar populations to generate the high-energy photons needed to doubly ionize oxygen. We extracted spectra for objects up to 2 mag fainter in the near-infrared than previous WFSS studies with the Hubble Space Telescope. Thus, this work clearly highlights the potential of JWST/NIRISS to provide high-quality WFSS data sets in crowded cluster environments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".