Analysis of the Herschel DEBRIS Sun-like star sample
Bibliographic record
Abstract
This paper presents a study of circumstellar debris around Sun-like stars using data from the Herschel DEBRIS Key Programme. DEBRIS is an unbiased survey comprising the nearest ∼90 stars of each spectral type A-M. Analysis of the 275 F-K stars shows that excess emission from a debris disc was detected around 47 stars, giving a detection rate of 17.1|$^{+2.6}_{-2.3}$| per cent, with lower rates for later spectral types. For each target a blackbody spectrum was fitted to the dust emission to determine its fractional luminosity and temperature. The derived underlying distribution of fractional luminosity versus blackbody radius in the population showed that most detected discs are concentrated at f ∼ 10−5 and at temperatures corresponding to blackbody radii 7–40 au, which scales to ∼40 au for realistic dust properties (similar to the current Kuiper belt). Two outlying populations are also evident; five stars have exceptionally bright emission ( f > 5 × 10−5), and one has unusually hot dust <4 au. The excess emission distributions at all wavelengths were fitted with a steady-state evolution model, showing that these are compatible with all stars being born with a narrow belt that then undergoes collisional grinding. However, the model cannot explain the hot dust systems – likely originating in transient events – and bright emission systems – arising potentially from atypically massive discs or recent stirring. The emission from the present-day Kuiper belt is predicted to be close to the median of the population, suggesting that half of stars have either depleted their Kuiper belts (similar to the Solar system) or had a lower planetesimal formation efficiency.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".