Estimating phase errors from pupil discontinuities from simulated on sky data: examples with VLT and Keck
Bibliographic record
Abstract
We propose and apply two methods for estimating phase discontinuities for two realistic scenarios on VLT and Keck. The methods use both phase diversity and a form of image sharpening. For the case of VLT, we simulate the `low wind effect' (LWE) which is responsible for focal plane errors in low wind and good seeing conditions. We successfully estimate the LWE using both methods, and show that using both methods both independently and together yields promising results. We also show the use of single image phase diversity in the LWE estimation, and show that it too yields promising results. Finally, we simulate segmented piston effects on Keck/NIRC2 images and successfully recover the induced phase errors using single image phase diversity. We also show that on Keck we can estimate both the segmented piston errors and any Zernike modes affiliated with the non-common path.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.000 |
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".