Scattered Light from Close‐in Extrasolar Planets: Prospects of Detection with the<i>MOST</i>Satellite
Bibliographic record
Abstract
The ultraprecise photometric space satellite MOST ( Microvariability and Oscillations of STars ) will provide the first opportunity to measure the albedos and scattered light curves from known short-period extrasolar planets. Because of the changing phases of an extrasolar planet as it orbits its parent star, the combined light of the planet-star system will vary on the order of tens of micromagnitudes. The amplitude and shape of the resulting light curve is sensitive to the planet's radius and orbital inclination, as well as the composition and size distribution of the scattering particles in the planet's atmosphere. To predict the capabilities of MOST and other planned space missions, we have constructed a series of models of such light curves, improving upon earlier work by incorporating more realistic details such as limb darkening of the star, intrinsic granulation noise in the star itself, tidal distortion and back-heating, higher angular resolution of the light scattering from the planet, and exploration of the significance of the angular size of the star as seen from the planet. We use photometric performance simulations of the MOST satellite, with the light-curve models as inputs, for one of the mission's primary targets, τ Boötis. These simulations demonstrate that, even adopting a very conservative signal detection limit of 4.2 μmag in amplitude (not power), we will be able to either detect the τ Boötis planet light curve or put severe constraints on possible extrasolar planet atmospheric models.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".