Three Direct Imaging Epochs Could Constrain the Orbit of Earth 2.0 inside the Habitable Zone
Bibliographic record
Abstract
Abstract Space-based direct imaging missions (HabEx, LUVOIR) would observe reflected light from exoplanets in the habitable zones of Sun-like stars. The ultimate—but not sole—goal of these concept missions is to characterize such planets. Knowing an exoplanet’s orbit would help twofold: (i) its semimajor axis informs whether the planet might harbor surface liquid water, making it a priority target; and (ii) predicting the planet’s future location would tell us where and when to look. The science yields of HabEx and LUVOIR depend on the number, cadence, and precision of observations required to establish a planet’s orbit. We produce mock observations using realistic distributions for the six Keplerian orbital parameters, experimenting with both beta and uniform eccentricity distributions, and accounting for imperfect astrometry (σ = 3.5 mas) and obscuration due to the inner working angle of a high-contrast imaging system (inner working angle = 31 mas). Using Markov chain Monte Carlo methods, we fit the orbital parameters, and retrieve their average precisions and accuracies as functions of cadence, number of epochs, and distance to the target. Given the time at which it was acquired, each image provides two data: the x and y position of the planet with respect to its star. Parameter retrieval based on one or two images is formally underconstrained, yet the semimajor axis posterior can be obtained semi-analytically. For a planet at 1 au around a star at a distance of 10 pc, three epochs constrain the semimajor axis to within ≲5%, if each image is taken at least 90 days apart.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".