Tidal Distortions as a Bottleneck on Constraining Exoplanet Compositions
Bibliographic record
Abstract
Abstract Improvements in the number of confirmed planets and the precision of observations imply a need to better understand subtle effects that may bias interpretations of exoplanet observations. One such effect is the distortion of a short period planet by its host star, affecting its derived density. We extend the work of Burton et al., Correia, and others, using a gravitational potential formulation to a sample of nearly 200 planets with periods less than 3 days. We find five planets exhibiting density variations of >10% and as many as 20 planets with deviations >5%. We derive an analytic approximation for this deviation as a function of the orbital period, transit depth, and mass ratio between the planet and host star, allowing for rapid determination of such tidal effects. We find that current density error bars are typically larger than tidal deviations but that reducing the uncertainty on transit depth and radial velocity (RV) amplitude by a factor of 3 causes tidal effects to dominate density errors (>50%) in >40% of planets in our sample, implying that in the near future upgraded observational precision will cause shape deviations to become a bottleneck with regards to analysis of exoplanet compositions. These two parameters are found to dominate uncertainties compared to errors on stellar mass and radius. We identify a group of eight planets (including WASP-19 b, HAT-P-7 b, and WASP-12 b) for which current density uncertainties are as much as 4× smaller than the potential shift due to tides, implying a possible 4σ bias on their density estimates.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".