Demographics of Protoplanetary Disks: A Simulated Population of Edge-on Systems
Bibliographic record
Abstract
Abstract The structure of protoplanetary disks plays an essential role in planet formation. A disk that is highly inclined, or “edge-on,” is of particular interest since its geometry provides a unique opportunity to study the disk’s vertical structure and radial extent. Candidate edge-on protoplanetary disks are typically identified via their unique spectral energy distributions (SEDs) and subsequently confirmed through high-resolution imaging. However, this selection process is likely biased toward the largest, most-massive disks, and the resulting sample may not accurately represent the underlying disk population. To investigate this, we generated a grid of protoplanetary disk models using radiative transfer simulations and determined which sets of disk parameters produce edge-on systems that could be recovered by the aforementioned detection techniques—i.e., identified by their SEDs and confirmed through follow-up imaging with the Hubble Space Telescope. In doing so, we adopt a quantitative working definition of “edge-on disks” (EODs) that is observation driven and agnostic about the disk inclination or other properties. Folding in empirical disk demographics, we predict an occurrence rate of 6.2% for EODs and quantify biases toward highly inclined, massive disks. We also find that EODs are underrepresented in samples of Spitzer-studied young stellar objects, particularly for disks with host masses of M ≲ 0.5 M ⊙. Overall, our analysis suggests that several dozen EODs remain undiscovered in nearby star-forming regions, and provides a universal selection process to identify EODs for consistent, population-level demographic studies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".