Off-axis aberration reconstruction of liquid mirror telescope based on neural network
Why this work is in the frame
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Bibliographic record
Abstract
The inherent aberrations existing at the off-axis angle of liquid mirror telescopes (LMT) limit their application in astronomy and other areas. In this paper, a new method of wavefront reconstruction based on neural networks is used to correct the off-axis aberrations in LMT. Firstly, the components of the off-axis aberration including defocus, astigmatism and coma which have the greatest influence on the imaging results, are analyzed. Then the nonlinear relationship between the aberrations and the image is discussed. Finally, a model using the convolutional neural network (CNN) is established to fit the nonlinear relationship. The results of network training and verification show that the network can predict the original aberration information quickly and accurately.
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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