Stellar multiplicity and debris discs: an unbiased sample
Bibliographic record
Abstract
Circumstellar dust discs have been observed around many nearby stars. However, many stars are part of binary or multiple stellar systems. A natural question arises regarding the presence and properties of such discs in systems with more than one star. To address this, we consider a sample of 449 systems (spectral types A–M) observed with the Herschel Space Observatory as part of the DEBRIS (Disc Emission via a Bias-free Reconnaissance in the Infrared/Submillimetre) programme. We have examined the stellar multiplicity of this sample by gathering information from the literature and performing an adaptive optics imaging survey at Lick Observatory. Five new companions were revealed with our programme. In total, we identify 188 (42 per cent) binary or multiple star systems. The multiplicity of the sample is examined with regards to the detection of circumstellar discs for stars of spectral types AFGK. In general, discs are less commonly detected around binaries than single stars, though the disc frequency is comparable among A stars regardless of multiplicity. However, this sample reveals the period distribution of disc-bearing binaries is consistent with that of non-disc binaries and with comparison field samples. We find that the properties of discs in binary systems are not statistically different from those around single stars. Although the frequency of disc-bearing FGK binaries may be lower than in single star systems, the processes behind disc formation and the characteristics of these discs are comparable among both populations.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".