Mid-to-late M Dwarfs Lack Jupiter Analogs
Bibliographic record
Abstract
Abstract Cold Jovian planets play an important role in sculpting the dynamical environment in which inner terrestrial planets form. The core accretion model predicts that giant planets cannot form around low-mass M dwarfs, although this idea has been challenged by recent planet discoveries. Here, we investigate the occurrence rate of giant planets around low-mass (0.1–0.3 M ⊙) M dwarfs. We monitor a volume-complete, inactive sample of 200 such stars located within 15 pc, collecting four high-resolution spectra of each M dwarf over six years and performing intensive follow-up monitoring of two candidate radial velocity variables. We use TRES on the 1.5 m telescope at the Fred Lawrence Whipple Observatory and CHIRON on the Cerro Tololo Inter-American Observatory 1.5 m telescope for our primary campaign, and MAROON-X on Gemini-North for high-precision follow up. We place a 95% confidence upper limit of 1.5% (68% confidence limit of 0.57%) on the occurrence of M P sin i > 1 M J giant planets out to the water snow line and provide additional constraints on the giant planet population as a function of M P sin i and period. Beyond the snow line (100 K < T eq < 150 K), we place 95% confidence upper limits of 1.5%, 1.7%, and 4.4% (68% confidence limits of 0.58%, 0.66%, and 1.7%) for 3 M J < M P sin i < 10 M J, 0.8 M J < M P sin i < 3 M J, and 0.3 M J < M P sin i < 0.8 M J giant planets, respectively; i.e., Jupiter analogs are rare around low-mass M dwarfs. In contrast, surveys of Sun-like stars have found that their giant planets are most common at these Jupiter-like instellations.
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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 | 0.001 | 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 | 0.001 |
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".