High contrast imaging of exoplanets on ELTs using a super-Nyquist wavefront control scheme
Bibliographic record
Abstract
One of the key science goals for extremely large telescopes (ELTs) is the detailed characterization of already known directly imaged exoplanets. The typical adaptive optics (AO) Nyquist control region for ELTs is ~0.4 arcseconds, placing many already known directly imaged planets outside the DM control region and not allowing any standard wavefront control scheme to remove speckles that would allow higher SNR images/spectra to be acquired. This can be fixed with super-Nyquist wavefront control (SNWFC), using a sine wave phase plate to allow for wavefront control outside the central DM Nyquist region. We demonstrate that SNWFC is feasible through a simple, deterministic, non-coronagraphic, super-Nyquist speckle nulling technique in the adaptive optics laboratory at the National Research Council of Canada. We also present results in simulation of how SNWFC using the self coherent camera (SCC) can be used for high contrast imaging. This technique could be implemented on future high contrast imaging instruments to improve contrast outside the standard central dark hole for higher SNR characterization of exoplanets.
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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 | 0.000 | 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) | 0.001 | 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".