Friends and Foes: The Conditional Occurrence of Planetary Companions to Transiting Exoplanets and their Impact on Radial Velocity Follow-up Observations
Bibliographic record
Abstract
Population models of Kepler's multi-planet systems have revealed patterns in their underlying architectures, which can be used to make predictions about the presence of additional planets in systems with known transiting planets. I will describe how we use such a model (He et al 2020) to compute the conditional occurrence of planets given a Kepler-detectable planet. While unseen planets may potentially be discovered by radial velocity (RV) follow-up observations, they can also add a source of systematic error in efforts to fit the semi-amplitude (K) of the transiting planet. I will show that measuring the K of the transiting planet when there are an unknown number of planets often requires significantly more observations than in the ideal case (when there are no additional planets). Planets around 10 day periods, common among the TESS planet candidates, with sizes of 1-2 Earth radii and K comparable to the single-measurement RV precision typically require ~100 observations to measure their K to within 20% error, compared to only ~60 observations in the ideal case. These results highlight a previously unaccounted for source of error when measuring the masses of transiting planets with RVs, such as in the follow-up of TESS planets.
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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 | 0.004 | 0.051 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".