Combining photometry and astrometry to improve orbit retrieval of directly imaged exoplanets
Bibliographic record
Abstract
ABSTRACT Future missions like Roman, HabEx, and LUVOIR will directly image exoplanets in reflected light. While current near-infrared direct imaging searches are only sensitive to young, self-luminous planets whose brightness is independent of their orbital phase, reflected light direct imaging will reveal changes in planet brightness throughout an orbit due to phase variations. One of the first objectives will be determining the planet’s orbit via astrometry, the projected position of the planet with respect to its host star in the sky plane. We show that phase variations can significantly improve the accuracy and precision of orbital retrieval with two or three direct images. This would speed up the classification of exoplanets and improve the efficiency of subsequent spectroscopic characterization. We develop a forward model to generate synthetic observations of the 2D astrometry and the planet/star flux ratio. Synthetic data are fitted with Keplerian orbits and Henyey–Greenstein phase variations to retrieve orbital and phase parameters. For astrometric uncertainties of 0.01 au in projected separation and flux ratio uncertainties of 10−12, using photometry in orbit retrieval improves the accuracy of semimajor axis by 47 per cent for two epochs and 61 per cent for three epochs if the phase curves have a known shape, but unknown amplitude. In a realistic scenario where phase curve shape and amplitude are a priori unknown, photometry improves accuracy by 16 per cent for two epochs and 50 per cent for three epochs. In general, we find that if the planetary flux is measured to better than 10σ at multiple epochs, it usefully contributes to orbit retrieval.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".