Astrometry and photometry in high contrast imaging: ADI/LOCI biases and the SOSIE/LOCI solution
Bibliographic record
Abstract
The direct exoplanet imaging field will strongly benefit from the higher angular resolution achieved by next generation 30+m telescopes. To fully take advantage of these new facilities, one of the biggest challenges that ground-based adaptive optics imaging must overcome is to derive accurate astrometry and photometry of point sources. The planet photometry and its astrometry are used to compare with atmospheric models and to fit orbits. If erroneous numbers are found, or if errors are underestimated, spurious fits can lead to unphysical planet characteristics or wrong/unstable orbits. Overestimating the errors also needs to be avoided as it degrades the value of the data. Several photometry/astrometry biases that are induced by advance imaging and processing techniques (such as ADI/SSDI/LOCI) are presented as well as a procedure to properly overcome those effects. These solutions will be implemented in the Gemini Planet Imager campaign data pipeline and it is expected that they will also play a crucial role in any future direct exoplanet imaging survey.
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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) | 0.001 | 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 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".