Experimental proof-of- concept adaptive optics system using a single-pixel camera
Bibliographic record
Abstract
In this work, experimental results are provided as a proof-of-concept for an adaptive optics (AO) system which estimates and corrects turbulence-induced atmospheric phase distortion using intensity measurements via a single-pixel camera (SPC). Atmospheric turbulence greatly degrades satellite-to-ground optical communication links, making an AO system an essential component. Whereas conventional AO approaches are optimized for visible bands, the use of an SPC allows for the use for longer wavelength near- and mid-infrared (IR) bands (e.g., 0.7 μ m - 8 μ m) which are less impacted by atmospheric perturbations than shorter wavelengths (e.g., visible light). This makes SPC-based AO systems promising candidates for satellite-to-ground optical communication particularly at mid-IR wavelengths which take advantage of higher optical bandwidth (relative to radio wavelengths) and lower turbulence strength (relative to near-IR and visible wavelengths). An experimental proof-of-concept demonstration of a satellite-to-ground optical link is presented where a spatial light modulator (SLM) modulates a laser beam to model atmospheric phase turbulence. Then, an SPC captures a focal plane intensity image where the phase turbulence is estimated and compensated via a closed-loop phase retrieval (PR) algorithm between the SLM, SPC, and a controller computer. The PR algorithm iteratively refines the wavefront phase correction based on measurement feedback in the loop which enables accurate compensation of phase distortions. This AO correction for satellite-to-ground optical links is essential to enable coupling of the received wavefront into a single-mode fiber (SMF). The effectiveness of the AO system for this application is evaluated through its SMF coupling efficiency. What we believe to be a new performance metric, analogous to the SMF coupling efficiency, is proposed, which can be measured using the same SPC setup eliminating the need of using an SMF in the PR loop of the experiment. The experimental measurements are shown to be compatible with the simulation results in which the equivalent coupling efficiency increases from less than 5% to 50% and 25% for the medium and strong turbulence regimes, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".