Model-based wavefront sensorless control with magnetic fluid deformable mirror for large aberration corrections
Bibliographic record
Abstract
Adaptive optics (AO) systems are used to enhance the performance of optical systems. A classical AO system consists of the wavefront corrector with the wavefront sensor (WFS). Wavefront correctors are able to compensate for aberrations in real-time with the measured aberrations. Compared with traditional wavefront correctors, the major advantage of the magnetic fluid deformable mirror (MFDM) features large deformation strokes that can be easily up to more than 100μm both for the single actuator or inter-actuators. However, the measuring range of WFS is normally small, which could limit its usage in the applications with large aberrations. Considering the idea of taking full advantages of the MFDM’s stroke strengths and the limitations of the AO system with the WFS, this paper proposes a model-based wavefront sensorless control algorithm for the adaptive optics systems with magnetic fluid deformable mirror. Compared with the model-free wavefront sensorless AO systems, the model-based control algorithm for the wavefront sensorless AO systems features faster convergence without dropping into the local optima. The model-based control approach is developed based on a relationship between the second moments of the wavefront gradients and the far-field intensity distribution by taking Zernike polynomials as the predetermined bias functions, therefore, the unknown aberrations can be corrected without the wavefront measurement in the closed-loop AO control system. The control algorithm is evaluated in a wavefront sensorless AO system setup with a prototype MFDM, where a parallel laser beam with unknown aberrations is supposed to produce a focused spot on the CCD. Experimental results show that the model-based control method can effectively make the MFDM to compensate for unknown aberrations in an imaging system
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".