Why Do M Dwarfs Have More Transiting Planets?
Bibliographic record
Abstract
Abstract We propose a planet formation scenario to explain the elevated occurrence rates of transiting planets around M dwarfs compared to Sun-like stars discovered by Kepler. We use a pebble drift and accretion model to simulate the growth of planet cores inside and outside of the snow line. A smaller pebble size interior to the snow line delays the growth of super-Earths, allowing giant planet cores in the outer disk to form first. When those giant planets reach pebble isolation mass they cut off the flow of pebbles to the inner disk and prevent the formation of close-in super-Earths. We apply this model to stars with masses between 0.1 and 2 M ⊙ and for a range of initial disk masses. We find that the masses of hot super-Earths and of cold giant planets are anticorrelated. The fraction of our simulations that form hot super-Earths is higher around lower-mass stars and matches the exoplanet occurrence rates from Kepler. The fraction of simulations forming cold giant planets is consistent with the stellar mass dependence from radial-velocity surveys. A key testable prediction of the pebble accretion hypothesis is that the occurrence rates of super-Earths should decrease again for M dwarfs near the substellar boundary like Trappist-1.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".