The International Deep Planet Survey I. The frequency of wide-orbit massive planets around A-stars
Bibliographic record
Abstract
Breakthrough direct detections of planetary companions orbiting A-type stars confirm the existence of massive planets at relatively large separations, but dedicated surveys are required to estimate the frequency of similar planetary systems. To measure the first estimation of the giant exoplanetary systems frequency at large orbital separation around A-stars, we have conducted a deep-imaging survey of young (8-400 Myr), nearby (19-84 pc) A- and F-stars to search for substellar companions in the 10-300 AU range. The sample of 42 stars combines all A-stars observed in previous AO planet search surveys reported in the literature with new AO observations from VLT/NaCo and Gemini/NIRI. It represents an initial subset of the International Deep Planet Survey (IDPS) sample of stars covering M- to B-stars. The data were obtained with diffraction-limited observations in H- and Ks-band combined with angular differential imaging to suppress the speckle noise of the central stars, resulting in typical 5-sigma detection limits in magnitude difference of 12 mag at 1, 14 mag at 2 and 16 mag at 5 which is sufficient to detect massive planets. A detailed statistical analysis of the survey results is performed using Monte Carlo simulations. Considering the planet detections, we estimate the fraction of A-stars having at least one massive planet (3-14 MJup) in the range 5-320 AU to be inside 5.9-18.8% at 68% confidence, assuming a flat distribution for the mass of the planets. By comparison, the brown dwarf (15-75 MJup) frequency for the sample is 2.0-8.9% at 68% confidence in the range 5-320 AU. Assuming power law distributions for the mass and semimajor axis of the planet population, the AO data are consistent with a declining number of massive planets with increasing orbital radius which is distinct from the rising slope inferred from radial velocity (RV) surveys around evolved A-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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".