Optimization of the optical sparse array configuration
Why this work is in the frame
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Bibliographic record
Abstract
One feature of systems with sparse optical aperture is the possible existence of zero-value regions of the optical transfer function. In using fast-convergent gradient methods for nonlinear constrained optimization with criteria based on the optical transfer function or the modulation transfer function, the gradient of the minimized function can also contain zero-value regions. Such situations can result in a suspension of the calculation if an intermediate solution falls into such a region. We show that, using gradient methods, it is possible to avoid this calculation problem if we redefine the subaperture step pupil functions—in particular, approximate them by continuously differentiable functions having no zero-value regions. This is demonstrated on examples of apertures consisting of circular and annular subapertures. This approach can be used for both types of multiaperture optical telescopes, the Michelson and Fizeau, and for both space and earth science missions.
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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 it