AI-powered low-order focal plane wavefront sensing in infrared
Why this work is in the frame
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Bibliographic record
Abstract
Adaptive optics (AO) systems are crucial for high-resolution astronomical observations by compensating for atmospheric turbulence. While laser guide stars (LGS) address high-order wavefront aberrations, natural guide stars (NGS) remain vital for low-order wavefront sensing (LOWFS). Conventional NGS-based methods like Shack-Hartmann sensors have limitations in field of view, sensitivity, and complexity. Focal plane wavefront sensing (FPWFS) offers advantages, including a wider field of view and enhanced signal-to-noise ratio, but accurately estimating low-order modes from distorted point spread functions (PSFs) remains challenging. We propose an AI-powered FPWFS method specifically for low-order mode estimation in infrared wavelengths. Our approach is trained on simulated data and validated on on-telescope data collected from the Keck I adaptive optic (K1AO) bench calibration source in K-band. By leveraging the enhanced signal-to-noise ratio in the infrared and the power of AI, our method overcomes the limitations of traditional LOWFS techniques.This study demonstrates the effectiveness of AI-based FPWFS for low-order wavefront sensing, paving the way for more compact, efficient, and high-performing AO systems for astronomical observations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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