Calculation of the Spherical and Chromatic Aberrations for Electrostatic Lenses Using Genetic Algorithm
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".