RESCUER: Cosmological K-corrections for star clusters
Bibliographic record
Abstract
The advent of JWST (the James Webb Space Telescope) now allows entire star cluster populations to be imaged in galaxies at cosmologically significant redshifts, bringing with it the need to apply K-corrections to their magnitudes and colour indices. Since the stellar populations within star clusters can be well approximated by a single age and metallicity, their spectral energy distributions are very different from those of galaxies or supernovae, and their K-corrections behave differently. We derive the photometric K-corrections versus redshift for model star clusters that cover a wide range of ages and metallicities, illustrating the results particularly for the broadband filters on the HST/ACS and the JWST/NIRCam cameras that are most commonly being used for imaging of populations of star clusters in distant galaxies. In an Appendix, we introduce a simple webtool called RESCUER that can generate K-values for any user-defined combination of cluster properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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; both teacher heads agree on what is shown here.
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".