Metal-Poor Star-Forming Clumps in Cosmic Noon Galaxies: Evidence for Gas Inflow and Chemical Dilution Using JWST NIRISS
Bibliographic record
Abstract
The formation and evolution of galaxies are intricately linked to the baryon cycle, which fuels star formation while shaping chemical abundances within galaxies. Investigating the relationship between star formation and metallicity for large samples of galaxies requires expensive IFU surveys or sophisticated tools to analyze grism data. Here we analyze JWST NIRISS slitless grism data using Sleuth, a tool that forward models and infers spatially resolved physical properties from grism data, including observations from JWST NIRISS/NIRCam and future grism data like that from the Roman Space Telescope. Sleuth enables extraction of high-quality emission line maps from slitless spectra, overcoming contamination and spatially varying stellar populations, which previously limited such studies. Utilizing Sleuth with data from the CAnadian NIRISS Unbiased Cluster Survey (CANUCS), we investigated the relationship between metallicity and star formation in the star-forming clumps of galaxies at 0.6 < z < 1.35. We analyzed a sample of 20 galaxies, extracted high-quality emission line maps with Sleuth, and analyzed, in detail, the spatially resolved properties of star-forming clumps. Using $Hα$, [SII], and [SIII] emission line maps, we examined the spatially resolved metallicities, ionization, and star formation rates of our sample. Our findings reveal that these star-forming clumps show lower metallicities ($\sim$ 0.1 dex) than their surrounding galactic environments, indicating a metallicity dilution of 20 $\%$ within the clumps' gas. Our analysis indicates that these clumps exhibit intensified star formation and reduced metallicity, likely due to the inflow of metal-poor gas. These clumps illustrate the dynamic relationship between star formation and chemical enrichment within galaxies.
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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 | 0.002 | 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 | 0.000 |
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".