Calculation of the Spherical and Chromatic Aberrations for Electrostatic Lenses Using Genetic Algorithm
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Bibliographic record
Abstract
Optical aberrations degrade the detecting performance in electron spectrometers.It is very difficult to calculate optical aberration parameters for complex electrostatic lens systems.In order to overcome this difficulty, the genetic algorithm method as a solution is introduced in this study.GAs are an intuitive research method based on the principle of generating new sequences of chromosomes in order to solve complex ordered problems.These algorithms target the global optimization of mathematical functions.This study uses a genetic algorithm to demonstrate the results of optimum aberration coefficients as a function of magnification for three-element electrostatic cylinder lenses.This algorithm is used to search for highperformance values.Different mutation and crossover probability values and also different selection and crossover types are tested.The optimum solution is obtained with a mutation rate of 0.01 and uniform crossover with a rate of 0.7.The proposed approach ensures the optimal solution for the aberration problems of the electrostatic lenses.
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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