Analysis and Optimization of Image Quality Evaluation Function in Computer-aided Alignment of Precision Optical System
Bibliographic record
Abstract
The consistent application of computer-aided alignment technology could surmount aimlessness and significantly shorten the align period of complex optical systems. Wherein, the appropriate system image evaluation function is the primary condition to solve the system misalignment successfully. This paper first establishes the relationship between the amount of misalignment and the system aberration through mathematical modelling and then analyzes the relationship between Zernike coefficient, geometric aberration and system image quality in detail, proposes to construct a system image evaluation system using the idea of Zernike coefficient weighting to solve the problem of numerical instability and imprecision of the solutions caused by the direct selection of Zernike coefficients. Subsequently, the experimental alignment of an infrared optical system was performed using the optical system image quality evaluation method established in this paper. The alignment results showed that the RMS value of the system image quality improved by 24.8% under the same solving algorithm compares to the direct selection of the Zernike coefficient and the weighted Zernike coefficient as the system image quality evaluation function. Therefore, the application of the weighted Zernike coefficient-based optical system image evaluation function proposed in this paper is reasonable, feasible, and effective in the computer-aided alignment process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".