Extend focal depth of a tracking lens by phase apodizers
Bibliographic record
Abstract
In a conventional particle tracking system, the depth of field is usually very small because of the use of high power imaging lens. The tracking accuracy may also be affected by the smearing of spot image, caused by focal shift. Increasing the focal depth and keeping uniform spot size in the focal region is highly desirable for a high accurate tracking system. In this report, we study the design of phase apdodizer that could be used to increase the focal depth of a tracking lens. The design is based on the requirements by highly accurate tracking that the point-spread function (PSF) of the lens keep an even and concentrated energy distribution when the lens is defocused. To achieve this purpose, a pure phase-shifting apodizer is introduced on the pupil plane of the lens. The function of the apodizer is to control the energy distribution of the 3D diffraction pattern near the focal region and make the effective spot size uniform or minimum variation along optical axis. The pattern of the 3D PSF of the lens with the apodizer shapes closed to a cylinder. New method used to search and optimize the design of the phase apodizer that meet the requirements of a particle tracking system will be studied. Theoretical analysis and numerical simulation are given in support of the method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".