High-contrast imaging with a self-coherent camera
Bibliographic record
Abstract
Direct imaging of exoplanets is very challenging because the planet is 104 to 1010 fainter than the star at a separation of a fraction of arcsec. Several coronagraphs have been proposed to reduce the contrast ratio but their performance strongly depends on the level of phase and amplitude aberrations that induce speckles in the science image. An active control of the aberrations and a posteriori calibration are thus required to reach very high contrasts. Classical adaptive optics are not sufficient for this purpose because of non-common path aberrations. Our team proposed a self-coherent camera that spatially modulates the speckles in the science image. It is then possible to both actively control a deformable mirror and calibrate the residuals a posteriori. The current paper is an overview of the developments we have been working on for 7 years. We present the principle of the self-coherent camera, laboratory performance obtained in monochromatic light, and upgrades of the technique to make it achromatic.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".