On the Effects of Planetary Oblateness on Exoplanet Studies
Bibliographic record
Abstract
Abstract When studying transiting exoplanets it is common to assume a spherical planet shape. However, short rotational periods can cause a planet to bulge at its equator, as is the case with Saturn, whose equatorial radius is almost 10% larger than its polar radius. As a new generation of instruments comes online, it is important to continually assess the underlying assumptions of models to ensure robust and accurate inferences. We analyze bulk samples of known transiting planets and calculate their expected signal strength if they were to be oblate. We find that for noise levels below 100 ppm, as many as 100 planets could have detectable oblateness. We also investigate the effects of fitting spherical planet models to synthetic oblate lightcurves. We find that this biases the retrieved parameters by several standard deviations for oblateness values >0.1–0.2. When attempting to fit an oblateness model to both spherical and oblate lightcurves, we find that the sensitivity of such fits is correlated with both the signal-to-noise ratio as well as the time sampling of the data, which can mask the oblateness signal. For typical values of these quantities for Kepler observations, it is difficult to rule out oblateness values less than ∼0.25. This results in an accuracy wall of 10%–15% for the density of planets which may be oblate. Finally, we find that a precessing oblate planet has the ability to mimic the signature of a long-period companion via transit-timing variations, inducing offsets at the level of tens of seconds.
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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 | 0.023 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".