DEBRIS DISKS IN<i>KEPLER</i>EXOPLANET SYSTEMS
Bibliographic record
Abstract
The Kepler mission recently identified 997 systems hosting candidate extrasolar planets, many of which are super-Earths. Realizing these planetary systems are candidates to host extrasolar asteroid belts, we use mid-infrared data from the Wide-field Infrared Survey Explorer ( WISE ) to search for emission from dust in these systems. We find excesses around eight stars, indicating the presence of warm to hot dust (∼100–500 K), corresponding to orbital distances of 0.1–10 AU for these solar-type stars. The strongest detection, KOI 1099, demands ∼500 K dust interior to the orbit of its exoplanet candidate. One star, KOI 904, may host very hot dust (∼1200 K, corresponding to 0.02 AU). Although the fraction of these exoplanet-bearing stars with detectable warm excesses (∼3%) is similar to that found by Spitzer surveys of solar-type field stars, the excesses detectable in the WISE data have much higher fractional luminosities ( L dust / L * ) than most known debris disks, implying that the fraction with debris disks of comparable luminosity may actually be significantly higher. It is difficult to explain the presence of dust so close to the host stars, generally corresponding to dust rings at radii <0.3 AU; both the collisional and Poynting–Robertson drag timescales to remove dust from the system are hundreds of years or less at these distances. Assuming a steady state for these systems implies large mass consumption rates with these short removal timescales, meaning that the dust production mechanism in these systems must almost certainly be episodic in nature.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".