Near-infrared characterization of ultra-diffuse galaxies in Abell 2744 by <i>JWST</i>/NIRISS imaging
Bibliographic record
Abstract
ABSTRACT We present a search and characterization of ultra-diffuse galaxies (UDGs) in the Frontier Fields cluster Abell 2744 at $z$ = 0.308. We use JWST/NIRISS F200W observations, acquired as part of the GLASS-JWST Early Release Science programme, aiming to characterize morphologies of cluster UDGs and their diffuse stellar components. A total number of 22 UDGs are identified by our selection criteria using morphological parameters, down to stellar mass of ∼107 M⊙. The selected UDGs are systematically larger in effective radius in F200W than in Hubble Space Telescope (HST)/ACS F814W images, which implies that some of them would not have been identified as UDGs when selected at rest-frame optical wavelengths. In fact, we find that about one-third of the UDGs were not previously identified based on the F814W data. We observe a flat distribution of the UDGs in the stellar mass–size plane, similar to what is found for cluster quiescent galaxies at comparable mass. Our pilot study using the new JWST F200W filter showcases the efficiency of searching UDGs at cosmological distances, with 1/30 of the exposure time of the previous deep observing campaign with HST. Further studies with JWST focusing on spatially resolved properties of individual sources will provide insight into their origin.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".