Using the Gerchberg–Saxton algorithm to reconstruct nonmodulated pyramid wavefront sensor measurements
Bibliographic record
Abstract
Context.Adaptive optics (AO) is a technique for improving the resolution of ground-based telescopes by correcting optical aberrations due to atmospheric turbulence and the telescope itself in real time. With the rise of giant segmented-mirror telescopes (GSMT), AO is needed more than ever to reach the full potential of these future observatories. One of the main performance drivers of an AO system is the wavefront-sensing operation, consisting of measuring the shape of the optical aberrations described above. Aims.The nonmodulated pyramid wavefront sensor (nPWFS) is a wavefront sensor with high sensitivity, allowing the limits of AO systems to be pushed. The high sensitivity comes at the expense of its dynamic range, which makes it a highly nonlinear sensor. We propose here a novel way to invert nPWFS signals by using the principle of reciprocity of light propagation and the Gerchberg-Saxton (GS) algorithm. Methods.We tested the performance of this reconstructor in two steps: the technique was first implemented in simulations, where some of its basic properties were studied. Then, the GS reconstructor was tested on the Santa Cruz Extreme Adaptive optics Laboratory (SEAL) testbed, located at the University of California Santa Cruz. Results.This new way to invert the nPWFS measurements allows us to drastically increase the dynamic range of the reconstruction for the nPWFS, pushing the dynamics close to a modulated PWFS. The reconstructor is an iterative algorithm with a high computational burden, which could be an issue for real-time purposes in its current implementation. However, this new reconstructor could still be helpful for various wavefront-control operations. This reconstruction technique has also been successfully tested on the Santa Cruz Extreme AO Laboratory (SEAL) bench, where it is now used as the standard way to invert nPWFS signal.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".