Imaging rotating and orbiting exoplanets with the solar gravitational lens
Bibliographic record
Abstract
ABSTRACT We utilize the well-established properties of the solar gravitational lens (SGL) to consider more realistic observational scenarios. Actual exoplanets, which may be the target of an SGL observational campaign, are not stationary. Their appearance also changes in a variety of ways, including changes due to their diurnal rotation and varying illumination due to their orbital motion around their host star. The nature of the SGL is such that imaging with one telescope is accomplished with a cadence of one pixel at a time, with substantial per-pixel integration times. Therefore, capturing a single snapshot of the target planet with a realistically sized telescope is not possible. Instead, the planetary surface must be reconstructed by inverting the combined effect of the SGL’s point-spread function and temporal changes induced by the planetary dynamics. Using the Earth as a stand-in, we demonstrate practical feasibility of this approach, by simulating a dynamical system and then recovering topographic images of acceptable quality. The dynamics-induced temporal variability of the exoplanet represents an added challenge, but even in the presence of such dynamics, use of the SGL for exoplanet imaging remains feasible.
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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 | 0.000 | 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 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".