Calculating non-scalar diffraction efficiently via merging Braunbek method and Bluestein method
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".