Observed Polarized Scattered Light Phase Functions of Planet-forming Disks
Bibliographic record
Abstract
Abstract Dust particles are the building blocks from which planetary bodies are made. A major goal of studies of planet-forming disks is to constrain the properties of dust particles and aggregates in order to trace their origin, structure, and the associated growth and mixing processes in the disk. Observations of the scattering and/or emission of dust in a location of the disk often lead to degenerate information about the kinds of particles involved, such as the size, porosity, or fractal dimensions of aggregates. Progress can be made by deriving the full (polarizing) scattering phase function of such particles at multiple wavelengths. This has now become possible by careful extraction from scattered light images. Such an extraction requires knowledge about the shape of the scattering surface in the disk, and we discuss how to obtain such knowledge as well as the associated uncertainties. We use a sample of disk images from observations with the Very Large Telescope/SPHERE to, for the first time, extract the phase functions of a whole sample of disks with broad phase-angle coverage. We find that polarized phase functions come in two categories. Comparing the extracted functions with theoretical predictions from rigorous T-Matrix computations of aggregates, we show that one category can be linked back to fractal, porous aggregates, while the other is consistent with more compact, less porous aggregates. We speculate that the more compact particles become visible in disks where embedded planets trigger enhanced vertical mixing.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".