Applications of the phase diversity technique to estimate the non-common path aberrations in the Gemini planet imager: results from simulation and real data
Bibliographic record
Abstract
We explore the application of phase diversity to calibrate the non common path aberrations (NCPA) in the Gemini Planet Imager (GPI). This is first investigated in simulation in order to characterize the ideal technique parameters with simulated GPI calibration source data. The best working simulation parameters are derived and we establish the algorithm's capability to recover an injected astigmatism. Furthermore, the real data appear to exhibit signs of de-centering between the in and out of focus images that are required by phase diversity; this effect can arise when the diverse images are acquired in closed loop and are close to the non-linear regime of the wavefront sensor. We show in simulation that this effect can inhibit our algorithm, which does not take into account the impact of de-centering between images. To mitigate this effect, we validate the technique of using a single diverse image with our algorithm; this is first demonstrated in simulation and then applied to the real GPI data. Following this approach, we find that we can successfully recover a known astigmatism injection using the real GPI data and subsequently apply an NCPA correction to GPI (in the format of offset reference slopes) to improve the relative Strehl ratio by 5%; we note this NCPA correction application is rudimentary and a more thorough application will be investigated in the near future. Finally, the estimated NCPA in the form of astigmatism and coma agree well with the magnitude of the same modes reported by Poyneer et al. 2016.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".