Petalling mode sensing for the Thirty Meter Telescope
Bibliographic record
Abstract
Petalling modes characterize the differential piston between the petals of the telescope’s aperture, typically separated by the secondary mirror support spiders. Since the discovery of petalling modes by the VLT in low wind conditions, addressing these modes has become a focal point, pertinent to nearly all telescopes equipped with high-performance adaptive optics systems. These modes are poorly sensed by mainstream wavefront sensors (WFS). There are three primary factors contributing to petalling modes in general. Firstly, turbulence discontinuity occurs across the spiders due to temperature non-uniformity, particularly in low wind conditions. This issue is partly mitigated by applying low emissivity coating, pioneered by the VLT. Computational fluid dynamics models used to assess the dome seeing aid in quantifying the residual effect. Secondly, phasing and stacking errors may arise in segmented mirrors. For TMT, the impact is negligible owing to the small width of the support spiders (22.5 cm) and highly redundant phasing sensors in the Alignment and Phasing System. Lastly, measurement noise may propagate to these modes, which we have observed when controlling two deformable mirrors in classic adaptive optics (AO) mode with a single pyramid WFS. Employing modal control with truncated modes is a simple and effective mitigation strategy without a notable performance penalty. Nevertheless, having a mechanism that can measure and control the petalling modes will provide reassurance of the AO system’s performance. In this paper, we present a novel hybrid iterative petalling sensor (HIPS) that utilizes modal based phase retrieval on time averaged PSFs from diffraction-limited tip/tilt/focus and full aperture low-order wavefront sensors, which breaks the even mode ambiguity. We successfully demonstrated this algorithm in both static and end-to-end closed-loop AO simulations.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".