Using Bayesian Deep Learning to Infer Planet Mass from Gaps in Protoplanetary Disks
Bibliographic record
Abstract
Abstract Planet-induced substructures, like annular gaps, observed in dust emission from protoplanetary disks, provide a unique probe for characterizing unseen young planets. While deep-learning-based models have an edge in characterizing a planet’s properties over traditional methods, such as customized simulations and empirical relations, they lacks the ability to quantify the uncertainties associated with their predictions. In this paper, we introduce a Bayesian deep-learning network, “DPNNet-Bayesian,” which can predict planet mass from disk gaps and also provides the uncertainties associated with the prediction. A unique feature of our approach is that it is able to distinguish between the uncertainty associated with the deep-learning architecture and the uncertainty inherent in the input data due to measurement noise. The model is trained on a data set generated from disk–planet simulations using the fargo3d hydrodynamics code, with a newly implemented fixed grain size module and improved initial conditions. The Bayesian framework enables the estimation of a gauge/confidence interval over the validity of the prediction, when applied to unknown observations. As a proof of concept, we apply DPNNet-Bayesian to the dust gaps observed in HL Tau. The network predicts masses of 86.0 ± 5.5 M ⊕, 43.8 ± 3.3 M ⊕, and 92.2 ± 5.1 M ⊕, respectively, which are comparable to those from other studies based on specialized simulations.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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".