Constraints on the Occurrence and Distribution of 1–20 M<sub>Jup</sub> Companions to Stars at Separations of 5–5000 au from a Compilation of Direct Imaging Surveys
Bibliographic record
Abstract
Abstract We present the first statistical analysis of exoplanet direct imaging surveys combining adaptive optics (AO) imaging at small separations with deep seeing-limited observations at large separations allowing us to study the entire orbital separation domain from 5 to 5000 au simultaneously. Our sample of 344 stars includes only confirmed members of nearby young associations and is based on all AO direct-imaging detection limits readily available online, with addition of our own previous seeing-limited surveys. Assuming that the companion distribution in mass and a semimajor axis follows a power-law distribution and adding a dependence on the mass of the host star, such as , we constrain the parameters to obtain , , and at a 68% confidence level, and we obtain for the overall planet occurrence rate for companions with masses between 1 and 20 in the range of 5–5000 au. Thus, we find that occurrence of companions is negatively correlated with a semimajor axis and companion mass (marginally) but is positively correlated with the stellar host mass. Our inferred mass distribution is in good agreement with other distributions found previously from direct imaging surveys for planets and brown dwarfs, but is shallower as a function of mass than the distributions inferred by radial velocity surveys of gas giants in the 1–3 au range. This may suggest that planets at these wide and very wide separations represent the low-mass tail of the brown dwarfs and stellar companion distribution rather than an extension of the distribution of the inner 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".