Systematic determination of dust properties for a sample of 133 spatially resolved debris discs
Bibliographic record
Abstract
ABSTRACT Determination of the composition and size distribution of dust grains in debris discs is strongly dependent on constraining the underlying spatial distribution of that dust through multiwavelength, spatially resolved imaging spanning near-infrared to millimetre wavelengths. To date, spatially resolved imaging exists for well over a hundred debris disc systems. Simple analytical radiative transfer models of debris dust emission can reveal trends in disc properties as a function of their host stars’ luminosities. Here, we present such an analysis for 133 debris discs, calculating the dust grain minimum sizes ($s_{\rm min}$), dust masses ($M_{\rm dust}$), and exponents of the size distribution (q) in conjunction with their architectures determined at far-infrared or millimetre wavelengths. The distribution of q at far-infrared to millimetre wavelengths is characterized, finding a value of $3.49^{+0.38}_{-0.33}$. We further newly identify a trend between q and $R_{\rm disc}$, which may be indicative of velocity dependent fragmentation, or grain growth at large radii. We find the disc masses inferred from this analysis are consistent with those of protoplanetary discs. Finally, we identify samples of debris discs suitable for further characterization at millimetre and centimetre wavelengths, expanding the number of spatially resolved systems upon which future studies of these statistics can be based.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".