Applying a Particle-only Model to the HL Tau Disk
Bibliographic record
Abstract
Abstract Observations have revealed rich structures in protoplanetary disks, offering clues about their embedded planets. Due to the complexities introduced by the abundance of gas in these disks, modeling their structure in detail is computationally intensive, requiring complex hydrodynamic codes and substantial computing power. It would be advantageous if computationally simpler models could provide some preliminary information on these disks. Here we apply a particle-only model (that we developed for gas-poor debris disks) to the gas-rich disk, HL Tauri, to address the question of whether such simple models can inform the study of these systems. Assuming three potentially embedded planets, we match HL Tau’s radial profile fairly well and derive best-fit planetary masses and orbital radii (0.40, 0.02, 0.21 Jupiter masses for the planets orbiting a 0.55 M ⊙ star at 11.22, 29.67, 64.23 au). Our derived parameters are comparable to those estimated by others, except for the mass of the second planet. Our simulations also reproduce some narrower gaps seen in the ALMA image away from the orbits of the planets. The nature of these gaps is debated but, based on our simulations, we argue they could result from planet–disk interactions via mean-motion resonances, and need not contain planets. Our results suggest that a simple particle-only model can be used as a first step to understanding dynamical structures in gas disks, particularly those formed by planets, and determine some parameters of their hidden planets, serving as useful initial inputs to hydrodynamic models which are needed to investigate disk and planet properties more thoroughly.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".