A First Look at Spatially Resolved Balmer Decrements at 1.0 < z < 2.4 from JWST NIRISS Slitless Spectroscopy
Bibliographic record
Abstract
Abstract We present the first results on the spatial distribution of dust attenuation at 1.0 < z < 2.4 traced by the Balmer decrement, Hα/Hβ, in emission-line galaxies using deep JWST NIRISS slitless spectroscopy from the CAnadian NIRISS Unbiased Cluster Survey (CANUCS). Hα and Hβ emission-line maps of emission-line galaxies are extracted and stacked in bins of stellar mass for two grism redshift bins, 1.0 < z grism < 1.7 and 1.7 < z grism < 2.4. Surface brightness profiles for the Balmer decrement are measured and radial profiles of the dust attenuation toward Hα, A Hα , are derived. In both redshift bins, the integrated Balmer decrement increases with stellar mass. Lower-mass (7.6 ≤ Log(M */M ⊙) < 10.0) galaxies have centrally concentrated, negative dust attenuation profiles whereas higher-mass galaxies (10.0 ≤ Log(M */M ⊙) < 11.1) have flat dust attenuation profiles. The total dust obscuration is mild, with on average 0.07 ± 0.07 and 0.14 ± 0.07 mag in the low- and high-redshift bins respectively. We model the typical light profiles of star-forming galaxies at these redshifts and stellar masses with GALFIT and apply both uniform and radially varying dust attenuation corrections based on our integrated Balmer decrements and radial dust attenuation profiles. If the Hα star formation rates (SFRs) of these galaxies were measured after slit-loss corrections assuming uniform dust attenuation with typical JWST NIRSpec slit spectroscopy (0.″2 × 0.″5 shutters), the total SFR will be overestimated by 6% ± 21% and 26% ± 9% at 1.0 ≤ z < 1.7 and 1.7 ≤ z < 2.4 respectively.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".