Photometry and astrometry with <i>JWST</i> – I. NIRCam point spread functions and the first <i>JWST</i> colour–magnitude diagrams of a globular cluster
Bibliographic record
Abstract
ABSTRACT As the James Webb Space Telescope (JWST) has become fully operational, early release data are now available to begin building the tools and calibrations for precision point-source photometry and astrometry in crowded cluster environments. Here, we present our independent reduction of NIRCam imaging of the metal-poor globular cluster M 92, which were collected under Director’s Discretionary Early Release Science programme ERS-1334. We derived empirical models of the point spread function (PSF) for filters F090W, F150W, F277W, and F444W, and find that these PSFs: (i) are generally undersampled (FWHM ∼ 2 pixel) in F150W and F444W and severely undersampled (FWHM ∼ 1 pixel) in F090W and F277W; (ii) have significant variation across the field of view, up to ∼15–20 per cent; and (iii) have temporal variations of ∼ 3–4 per cent across multi-epoch exposures. We deployed our PSFs to determine the photometric precision of NIRCam for stars in the crowded, central regions of M 92, measured to be at the ∼0.01 mag level. We use these data to construct the first JWST colour–magnitude diagrams of a globular cluster. Employing existing stellar models, we find that the data reach almost the bottom of the M 92 main sequence (∼0.1 M⊙), and reveal 24 white dwarf candidate members of M 92 in the brightest portion of the white dwarf cooling sequence. The latter are confirmed through a cross-match with archival HST UV and optical data. We also detect the presence of multiple stellar populations along the low-mass main sequence of M 92.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".