Analyzing the atmospheric dispersion correction of the Gemini Planet Imager: residual dispersion above design requirements
Bibliographic record
Abstract
The atmospheric dispersion corrector (ADC) of the Gemini Planet Imager (GPI) corrects the chromatic dispersion caused by differential atmospheric refraction (DAR), making it an important optic for exoplanet observation. Despite requiring <5 mas of residual DAR to avoid potentially affecting the coronagraph, the GPI ADC averages ∼7 and ∼11 mas of residual DAR in H and J band, respectively. We analyzed GPI data in those bands to find explanations for the underperformance. We found the model GPI uses to predict DAR underestimates humidity’s impact on incident DAR, causing on average a 0.54 mas increase in H band residual DAR. Additionally, the GPI ADC consistently undercorrects in H band by about 7 mas, causing almost all the H band residual DAR. J band does not have such an offset. Perpendicular dispersion induced by the GPI ADC, potentially from a misalignment in the prisms’ relative orientation, causes 86% of the residual DAR in J band. Correcting these issues could reduce residual DAR, thereby improving exoplanet detection. We also made an approximation for the index of refraction of air from 0.7 to 1.36 microns that more accurately accounts for the effects of humidity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".