Calculating non-scalar diffraction efficiently via merging Braunbek method and Bluestein method
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Bibliographic record
Abstract
Diffraction is an optical phenomenon that is commonly investigated for its applications in many optical systems, such as diffractive optical elements, microscopy, and coronagraphs. Current models for predicting diffraction typically suffer from either efficiency or accuracy. This paper addressed both issues by implementing techniques inspired by Braunbek method and Bluestein method. A modification to the Kirchhoff’s boundary conditions is used to improve the theoretical model, and the Chirp-z transform is applied instead of the fast Fourier transform for more flexible calculations. A comparison between diffraction patterns for different models shows that the new method exceeds in accuracy. A comparison of time between numerical methods demonstrates that the chirp-z transform is faster in computation than the fast Fourier transform by about a minute. The method introduced provides many implications, such as the enhancement of dynamic optical systems and the improvement of flexibility in other realms of numerical Fourier transform.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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