Incorporating adaptive optics controls history in post-processing of ground-based coronagraph models
Bibliographic record
Abstract
The planet detection thresholds of space-based coronagraphs are predicted to lie within an order of magnitude from their theoretical (shot-noise) limits. Ground-based telescopes, on the other hand, are limited by larger systematic uncertainties in the point spread function (PSF) of the residual light which rapidly fluctuates due to atmospheric turbulence. The PSF is affected by Adaptive Optics (AO) which reduce the intensity of the speckles but also make them less predictable. Although not a common practice, it is possible to take millisecond exposures of the so-called “frozen” speckles and record the history of AO controls, in which case the collected data resembles that of simulated space coronagraphs. In this work we use the HEEPS simulation of the EELT/METIS to assess the applicability of this newly-developed space-oriented approach to ground-based postprocessing. Unlike intensity-based algorithms, this method formulates the estimation problem in terms of the electric field of the speckles and therefore can incorporate controls history and various temporal models of the electric field variations. In our simulations, we artificially introduced small deformable mirror (DM) probes on top of AO controls, and achieved a post-processing error lower by a factor of 2 than that of Angular Differential Imaging (ADI). However, our attempt at incorporating the AO history without DM probes, has so far resulted in higher errors than ADI.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".