The coronagraphic Modal Wavefront Sensor: a hybrid focal-plane sensor for the high-contrast imaging of circumstellar environments
Bibliographic record
Abstract
The raw coronagraphic performance of current high-contrast imaging instruments is limited by the presence of a quasi-static speckle (QSS) background, resulting from instrumental Non-Common Path Errors (NCPEs). Rapid development of efficient speckle subtraction techniques in data reduction has enabled final contrasts of up to 10-6 to be obtained, however it remains preferable to eliminate the underlying NCPEs at the source. In this work we introduce the coronagraphic Modal Wavefront Sensor (cMWS), a new wavefront sensor suitable for real-time NCPE correction. This combines the Apodizing Phase Plate (APP) coronagraph with a holographic modal wavefront sensor to provide simultaneous coronagraphic imaging and focal-plane wavefront sensing with the science point-spread function. We first characterise the baseline performance of the cMWS via idealised closed-loop simulations, showing that the sensor is able to successfully recover diffraction-limited coronagraph performance over an effective dynamic range of ±2.5 radians root-mean-square (rms) wavefront error within 2–10 iterations, with performance independent of the specific choice of mode basis. We then present the results of initial on-sky testing at the William Herschel Telescope, which demonstrate that the sensor is capable of NCPE sensing under realistic seeing conditions via the recovery of known static aberrations to an accuracy of 10 nm (0.1 radians) rms error in the presence of a dominant atmospheric speckle foreground. We also find that the sensor is capable of real-time measurement of broadband atmospheric wavefront variance (50% bandwidth, 158 nm rms wavefront error) at a cadence of 50 Hz over an uncorrected telescope sub-aperture. When combined with a suitable closed-loop adaptive optics system, the cMWS holds the potential to deliver an improvement of up to two orders of magnitude over the uncorrected QSS floor. Such a sensor would be eminently suitable for the direct imaging and spectroscopy of exoplanets with both existing and future instruments, including EPICS and METIS for the E-ELT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".