Bibliographic record
Abstract
In the high-density environments of circumstellar disks dust grains are expected to grow to large sizes by coagulation. Somewhat unexpectedly, recent near-IR observations of PAH features from disks around Herbig Ae/Be stars demonstrate that a substantial amount of dust mass in the surface layers of these disks (up to several tens of percent of the local carbon content) can be locked up in small nanometer-sized particles. We investigate the possibility of detecting the electric dipole emission produced by these nanoparticles (sizes ~1 nm) as they spin at thermal rates (tens of GHz) in cold gas. We show that such emission peaks in the microwave range and dominates over the thermal disk emission at ν ≲ 50 GHz typically by a factor of several if ≳5% of the total carbon abundance is locked up in nanoparticles. We test the sensitivity of this prediction to various stellar and disk parameters and show that if the potential contamination of the spinning dust component by the free-free and/or synchrotron emission can be removed, then the best chances of detecting this emission would be in disks with small opacities, having SEDs with steep submillimeter slopes (which minimize thermal disk emission at GHz frequencies). Detection of the spinning dust emission would provide important evidence for the existence, properties, and origin of the population of small dust particles in protoplanetary disks, with possible ramifications for planet formation.
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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 | 0.000 |
| 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) | 0.001 | 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".