MASSIVE: A Bayesian analysis of giant planet populations around low-mass stars
Bibliographic record
Abstract
Context.Direct imaging has led to the discovery of several giant planet and brown dwarf companions.These imaged companions populate a mass, separation and age domain (mass > 1 M Jup , orbits > 5 AU, age < 1 Gyr) quite distinct from the one occupied by exoplanets discovered by the radial velocity or transit methods.This distinction could indicate that different formation mechanisms are at play.Aims.We aim at investigating correlations between the host star's mass and the presence of wide-orbit giant planets, and at providing new observational constraints on planetary formation models.Methods.We observed 58 young and nearby M-type dwarfs in L -band with the VLT/NaCo instrument and used angular differential imaging algorithms to optimize the sensitivity to planetary-mass companions and to derive the best detection limits.We estimate the probability of detecting a planet as a function of its mass and physical separation around each target.We conduct a Bayesian analysis to determine the frequency of substellar companions orbiting low-mass stars, using a homogenous sub-sample of 54 stars.Results.We derive a frequency of 4.4 +3.2 -1.3 % for companions with masses in the range of 2-80 M Jup , and 2.3 +2.9 -0.7 % for planetary mass companions (2-14 M Jup ), at physical separations of 8 to 400 AU for both cases.Comparing our results with a previous survey targeting more massive stars, we find evidence that substellar companions more massive than 1 M Jup with a low mass ratio Q with respect to their host star (Q < 1%), are less frequent around low-mass stars.This may represent observational evidence that the frequency of imaged wide-orbit substellar companions is correlated with stellar mass, corroborating theoretical expectations.Contrarily, we show statistical evidence that intermediate-mass ratio (1% < Q < 5%) companion with masses >2 M Jup might be independent from the mass of the host star.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".