Small Planets around Cool Dwarfs: Enhanced Formation Efficiency of Super-Earths around M Dwarfs
Bibliographic record
Abstract
Abstract Current measurements of planet population as a function of stellar mass show three seemingly contradictory signatures: close-in super-Earths are more prevalent around M dwarfs than FGK dwarfs; inner super-Earths are correlated with outer giants; and outer giants are less common around M dwarfs than FGK dwarfs. Here, we build a simple framework that combines the theory of pebble accretion with the measurements of dust masses in protoplanetary disks to reconcile all three observations. First, we show that cooler stars are more efficient at converting pebbles into planetary cores at short orbital periods. Second, when disks are massive enough to nucleate a heavy core at 5 au, more than enough dust can drift in to assemble inner planets, establishing the correlation between inner planets and outer giants. Finally, while stars of varying masses are similarly capable of converting pebbles into cores at long orbital periods, hotter stars are much more likely to harbor more massive dust disks so that the giant planet occurrence rate rises around hotter stars. Our results are valid over a wide range of parameter space for a disk accretion rate that follows M ̇ ⋆ ∼ 10 − 8 M ⊙ yr − 1 ( M ⋆ / M ⊙ ) 2 . We predict a decline in mini-Neptune population (but not necessarily terrestrial planets) around stars lighter than ∼0.3–0.5M ⊙. Cold giants (≳5 au), if they exist, should remain correlated with inner planets even around lower-mass stars.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".