High-Contrast Imaging and Wavefront Control with a PIAA Coronagraph: Laboratory System Validation
Bibliographic record
Abstract
The Phase-Induced Amplitude Apodization (PIAA) coronagraph is a high-performance coronagraph concept able to work at small angular separation with little loss in throughput. We present results obtained with a laboratory PIAA system including active wavefront control. The system has a 94.3% throughput (excluding coating losses) and operates in air with monochromatic light. Our testbed achieved a 2.27 × 10 -7 raw contrast between 1.65λ/ D (inner working angle of the coronagraph configuration tested) and 4.4λ/ D (outer working angle). Through careful calibration, we were able to separate this residual light into a dynamic coherent component (turbulence, vibrations) at 4.5 × 10 -8 contrast and a static incoherent component (ghosts and/or polarization mismatch) at 1.6 × 10 -7 contrast. Pointing errors are controlled at the 10 -3 λ/ D level using a dedicated low-order wavefront sensor. While not sufficient for direct imaging of Earthlike planets from space, the 2.27 × 10 -7 raw contrast achieved already exceeds requirements for a ground-based extreme adaptive optics system aimed at direct detection of more massive exoplanets. We show that over a 4 hr period, averaged wavefront errors have been controlled to the 3.5 × 10 -9 contrast level. This result is particularly encouraging for ground-based extreme-AO systems relying on long-term stability and absence of static wavefront errors to recover planets much fainter than the fast boiling speckle halo.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".