When, where, and how star formation happens in a galaxy pair at cosmic noon using CANUCS <i>JWST</i>/NIRISS grism spectroscopy
Bibliographic record
Abstract
ABSTRACT Spatially resolved studies are key to understanding when, where, and how stars form within galaxies. Using slitless grism spectra and broad-band imaging from the CAnadian NIRISS Unbiased Cluster Survey (CANUCS), we study the spatially resolved properties of a strongly lensed (μ = 5.4$\pm$1.8) z = 0.8718 galaxy pair consisting of a blue face-on galaxy (10.2 $\pm$ 0.2 log($M/M_\odot$)) with multiple star-forming clumps and a dusty red edge-on galaxy (9.9 $\pm$ 0.3 log($M/M_\odot$)). We produce accurate H $\alpha$ maps from JWST/NIRISS grism data using a new methodology that accurately models spatially varying continuum and emission line strengths. With spatially resolved indicators, we probe star formation on time-scales of $\sim$10 Myr (NIRISS H $\alpha$ emission line maps) and $\sim$100 Myr (UV imaging and broad-band SED fits). Taking the ratio of the H $\alpha$ to UV flux ($\eta$), we measure spatially resolved star formation burstiness. We find that in the face-on galaxy both H $\alpha$ and broad-band star formation rates (SFRs) drop at large galactocentric radii by a factor of $\sim$4.7 and 3.8, respectively, while SFR over the last $\sim$100 Myrs has increased by a factor of 1.6. Additionally, of the 20 clumps identified in the galaxy pair we find that 7 are experiencing bursty star formation, while 10 clumps are quenching, and 3 are in equilibrium (either being in a state of steady star formation or post-burst). Our analysis reveals that the blue face-on galaxy disc is predominantly in a quenching or equilibrium phase. However, the most intense quenching within the galaxy is seen in the quenching clumps. This pilot study demonstrates what JWST/NIRISS data can reveal about spatially varying star formation in galaxies at Cosmic Noon.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".