Control System Performance of a Woofer-Tweeter Adaptive Optics System
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Bibliographic record
Abstract
A simple adaptive optics system for astronomy uses a single wave front sensor (WFS) and a single deformable mirror (DM) to correct for the distortions imposed on light by the atmosphere and the static aberrations of the telescope optics [1]. In the next generation telescopes, both the actuator density and maximum actuator stroke requirements increase significantly due to the enormity of these very large telescopes. Current technology is cost prohibitive to design a single mirror that satisfies both of these requirements. Fortunately, the large stroke required is for the compensation of low spatial frequency distortion [2]. This allows the system to be designed with two DMs; (i) a high stroke, low actuator density DM named the Woofer and (ii) a low stroke, high actuator density DM named the Tweeter. The Adaptive Optics Laboratory at the University of Victoria has recently produced a test bench for this Woofer-Tweeter system. This project is part of the development of the Thirty Meter Telescope (TMT) that will be built in the next decade. Initial simulated and experimental results have shown that the developed controller can appropriately split the correction between the mirrors and acts similarly to the single DM case shown in [3]. This paper focuses primarily on discrete control and the Z-domain [4]. The Woofer corrects for the low-spatial-low-temporal frequency disturbances and the Tweeter corrects for the remaining disturbance. It has been assumed that the Woofer can respond slower than the tweeter. The Woofer’s impulse response is modeled as an exponential decay, e-kT/τ. A one dimensional representation of the controller approach is shown in Figure 1. The Woofer slowly approaches the steady state of the input signal. During this time, the Tweeter compensates for the residual error. The combined response of the two DMs is then equal to how the single DM case would respond to the input. A simple layout of the system components is shown in Figure 2.
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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