Wavefront reconstruction over a circular aperture using gradient data extrapolated via the mirror equations
Bibliographic record
Abstract
Methods for extrapolating gradient data outside a circular aperture from measurements obtained within a circular aperture are presented. The proposed methods are required to be computationally efficient and to avoid the excitation of additional waffle modes in Fried alignment. It is shown that, using an octagon as an intermediate step from the circle to the square in the extrapolation process, the computations or residual reconstruction error can be reduced. The resulting computational cost is as low as O(N(1/2)), where N is the number of measurement points. The performances of the extrapolation methods are studied in connection with a recently developed O(N) wavefront reconstruction algorithm based on wavelet filter banks [IEEE J. Sel. Top. Signal Process. 2, 781 (2008)] Experiments indicate that, as expected, there is a significant reconstruction error if no extrapolation is used. Further, the proposed extrapolation techniques lead to a reconstruction with data that are marginally different from a pupil masked reconstruction using data from a square aperture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".