Pan-Planets: Searching for hot Jupiters around cool dwarfs
Bibliographic record
Abstract
The Pan-Planets survey observed an area of 42 sq deg. in the galactic disk for about 165 h. The main scientific goal of the project is the detection of transiting planets around M dwarfs. We establish an efficient procedure for determining the stellar parameters Teff and log g of all sources using a method based on SED fitting, utilizing a three-dimensional dust map and proper motion information. In this way we identify more than 60 000 M dwarfs, which is by far the largest sample of low-mass stars observed in a transit survey to date. We present several planet candidates around M dwarfs and hotter stars that are currently being followed up. Using Monte Carlo simulations we calculate the detection efficiency of the Pan-Planets survey for different stellar and planetary populations. We expect to find 3.0+3.3-1.6 hot Jupiters around F, G, and K dwarfs with periods lower than 10 days based on the planet occurrence rates derived in previous surveys. For M dwarfs, the percentage of stars with a hot Jupiter is under debate. Theoretical models expect a lower occurrence rate than for larger main sequence stars. However, radial velocity surveys find upper limits of about 1% due to their small sample, while the Kepler survey finds a occurrence rate that we estimate to be at least 0.17b(+0.67-0.04) %, making it even higher than the determined fraction from OGLE-III for F, G and K stellar types, 0.14 (+0.15-0.076) %. With the large sample size of Pan-Planets, we are able to determine an occurrence rate of 0.11 (+0.37-0.02) % in case one of our candidates turns out to be a real detection. If, however, none of our candidates turn out to be true planets, we are able to put an upper limit of 0.34% with a 95% confidence on the hot Jupiter occurrence rate of M dwarfs. This limit is a significant improvement over previous estimates where the lowest limit published so far is 1.1% found in the WFCAM Transit Survey. Therefore we cannot yet confirm the theoretical prediction of a lower occurrence rate for cool 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 | 0.001 | 0.001 |
| 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".