An unbiased study of debris discs around A-type stars with Herschel
Bibliographic record
Abstract
The Herschel DEBRIS (Disc Emission via a Bias-free Reconnaissance in the Infrared/Submillimetre) survey brings us a unique perspective on the study of debris discs around main-sequence A-type stars. Bias-free by design, the survey offers a remarkable data set with which to investigate the cold disc properties. The statistical analysis of the 100 and 160 μm data for 86 main-sequence A stars yields a lower than previously found debris disc rate. Considering better than 3σ excess sources, we find a detection rate ≥24 ± 5 per cent at 100 μm which is similar to the debris disc rate around main-sequence F/G/K-spectral type stars. While the 100 and 160 μm excesses slowly decline with time, debris discs with large excesses are found around some of the oldest A stars in our sample, evidence that the debris phenomenon can survive throughout the length of the main sequence (˜1 Gyr). Debris discs are predominantly detected around the youngest and hottest stars in our sample. Stellar properties such as metallicity are found to have no effect on the debris disc incidence. Debris discs are found around A stars in single systems and multiple systems at similar rates. While tight and wide binaries (<1 and >100 au, respectively) host debris discs with a similar frequency and global properties, no intermediate separation debris systems were detected in our sample.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".