Point-spread Function Ramifications and Deconvolution of a Signal Dependent Blur Kernel Due to Interpixel Capacitive Coupling
Bibliographic record
Abstract
Interpixel capacitance (IPC) is a deterministic electronic coupling that results in a portion of the collected signal incident on one pixel of a hybridized detector array being measured in adjacent pixels. Data collected by light sensitive HgCdTe arrays which exhibit this coupling typically goes uncorrected or is corrected by treating the coupling as a fixed point-spread function. Evidence suggests that this IPC coupling is not uniform across different signal and background levels. This variation invalidates assumptions that are key in decoupling techniques such as Wiener Filtering or application of the Lucy–Richardson algorithm. Additionally, the variable IPC results in the point-spread function (PSF) depending upon a star's signal level relative to the background level, among other parameters. With an IPC ranging from 0.68% to 1.45% over the full well depth of a sensor, as is a reasonable range for the H2RG arrays, the FWHM of the JWSTs NIRCam 405N band is degraded from 2.080 pix (0 132) as expected from the diffraction pattern to 2.186 pix (0 142) when the star is just breaching the sensitivity limit of the system. For example, When attempting to use a fixed PSF fitting (e.g., assuming the PSF observed from a bright star in the field) to untangle two sources with a flux ratio of 4:1 and a center to center distance of 3 pixels, flux estimation can be off by upwards of 1.5% with a separation error of 50 millipixels. To deal with this issue an iterative non-stationary method for deconvolution is here proposed, implemented, and evaluated that can account for the signal dependent nature of IPC.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".