JWST’s PEARLS: Improved Flux Calibration for NIRCam
Bibliographic record
Abstract
Abstract The Prime Extragalactic Areas for Reionization and Lensing Science, a James Webb Space Telescope (JWST) GTO program, obtained a set of unique NIRCam observations that have enabled us to significantly improve the default photometric calibration across both NIRCam modules. The observations consisted of three epochs of 4-band (F150W, F200W, F356W, and F444W) NIRCam imaging in the Spitzer IRAC Dark Field (IDF). The three epochs were six months apart and spanned the full duration of Cycle 1. As the IDF is in the JWST continuous viewing zone, we were able to design the observations such that the two modules of NIRCam, modules A and B, were flipped by 180° and completely overlapped each other’s footprints in alternate epochs. We were therefore able to directly compare the photometry of the same objects observed with different modules and detectors, and we found significant photometric residuals up to ∼0.05 mag in some detectors and filters, for the default version of the calibration files that we used (jwst_1039.pmap). Moreover, there are multiplicative gradients present in the data obtained in the two long-wavelength bands. The problem is less severe in the data reduced using the latest pmap (jwst_1130.pmap as of 2023 September), but it is still present, and is non-negligible. We provide a recipe to correct for this systematic effect to bring the two modules onto a more consistent calibration, to a photometric precision better than ∼0.02 mag.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".