Modeling and subtracting diffuse cluster light in JWST images
Bibliographic record
Abstract
We present a methodology for modeling and removing light from cluster galaxies and intracluster light (ICL) from James Webb Space Telescope images of gravitational lensing clusters. We apply our method to Webb’s First Deep Field the SMACS 0723 Early Release Observations and use the ICL-subtracted images to select a sample of globular clusters (GCs) and dwarf galaxies within the cluster. We compare the spatial distributions of these two samples with our models of the galaxy and ICL light, finding significant similarities. In particular, we find that GCs trace the diffuse ICL, while dwarf galaxies are centrally concentrated near the cluster center We quantify the relationship between the surface density of compact sources and total cluster light, demonstrating a significant, tight correlation. We repeat our methodology and compare distributions of GCs with the dark matter surface density and find a comparable result. Our findings suggest a common origin for GCs and diffuse ICL, with stripping from massive galaxies as they merge with the cluster being a plausible scenario.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".