Focal-plane wavefront control using deep learning for high-contrast imaging
Bibliographic record
Abstract
On ground-based telescopes, high contrast imaging (HCI) systems suffer the common limitations of atmospheric turbulence that produce phase aberrations on the wavefront. While Adaptive Optics (AO) systems are adept at correcting these aberrations, non-common path aberrations (NCPAs) require additional intervention. NCPAs are caused by the wavefront sensor (WFS) measuring and correcting for a wavefront that is different from the wavefront affecting the science images. These aberrations introduce biases to observations that can be misinterpreted as exoplanets. In the past years we have developed focal-plane wavefront sensing (FPWFS) for vortex coronagraphs, exploring various techniques to lift the sign-ambiguity on even Zernike modes and to estimate NCPAs. Using Deep Learning (DL) algorithms, we have trained models on large laboratory datasets of Zernike coefficients with their associated images, and we have used those models to identify and correct aberrations (Quesnel 2024). Here, we build upon our previous approaches by replacing the DL algorithms with Reinforcement Learning (RL) that allow the real-time training and correction of NCPAs such as water vapor seeing. We have created a simulation that mimics the Mid-infrared ELT Imager and Spectrograph (METIS) and uses RL algorithms to correct the simulated aberrations caused by water vapor seeing. We are currently fine-tuning RL algorithms with the aim to eventually conduct on-sky demonstrations. The development and on-sky demonstration of this framework would be a major milestone for the deployment of FPWFS on METIS and could prove highly valuable for future generations of HCI instruments.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".