Astrometric errors introduced by interpixel capacitive coupling in hybridized arrays
Bibliographic record
Abstract
Interpixel capacitance (IPC) between adjacent pixels in hybridized arrays gives rise to an electrostatic cross talk. This cross talk causes MTF degradation and blurring of images or spectra collected using these devices. As pixel size is driven down from the 18-μm pixel pitch of the H2RG read out circuits to the 10- or 15-μm H4RGs IPC is driven up resulting in greater cross talk, all else being equal. Mounting evidence indicates that IPC varies as a function of depletion state of the photo-active diodes. For single pixel events, increasing the event intensity corresponds to a decreasing fractional coupling. If left uncorrected, IPC can give rise to systematic errors in precision astrometric and photometric measurements, in particular when dealing with confused point sources or spatially extended structures for shape measurements as demonstrated through comparison of registered sources from ESO HAWK-I and HST ACS WFC datasets. Furthermore, these errors will be the most significant when operating near the sensitivity limit of these devices. Deconvolution-based correction methods are invalidated by this same signal dependence. Instead, a numerical method of successive approximation can be used to correct coupling due to a well-characterized IPC. Examination of single pixel reset data above flat fields could be used to characterize IPC’s functional relationship for neighboring pixels. This higher quality characterization can result in more accurate correction.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".