An Algorithm to Mitigate Charge Migration Effects in Data from the Near Infrared Imager and Slitless Spectrograph on the James Webb Space Telescope*
Bibliographic record
Abstract
Abstract We present an algorithm that mitigates the effects of charge migration due to the “brighter-fatter effect” (BFE) that occurs for highly illuminated stars in the Teledyne HAWAII-2RG detectors used in the NIRCam, NIRISS, and NIRSpec science instruments aboard the James Webb Space Telescope (JWST). The impact of this effect is most significant for photometry and spectrophotometry of bright stars in data for which the point-spread function (PSF) is undersampled, which is the case for several observing modes of the NIRISS instrument. The main impact of BFE to NIRISS data is incorrect count rate determinations for pixels in the central regions of PSFs of bright stars due to jump detections that are caused by charge migration from peak pixels to surrounding pixels. The effect is especially significant for bright compact sources in resampled, distortion-free images produced by the drizzle algorithm: quantitatively, apparent flux losses of >50% can occur in such images due to BFE. We describe the algorithm of the “charge_migration” mitigation step that has been implemented in version 10.0 of the operational JWST calibration pipeline as of 2023 December 5. We illustrate the impact of this step in terms of the resulting improvements of the precision of imaging photometry of point sources. The algorithm renders the effects of BFE on photometry and surface brightness measurements to stay within 1%.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".