# Using Bayesian Deep Learning to infer Planet Mass from Gaps in Protoplanetary Disks

arXiv: https://arxiv.org/abs/2202.11730 · HTML: https://mlsanity.com/paper/2202.11730/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [DPNNet-2.0 Part I: Finding hidden planets from simulated images of protoplanetary disk gaps](https://mlsanity.com/paper/2107.09086/) (2021)
- [PGNets: Planet mass prediction using convolutional neural networks for radio continuum observations of protoplanetary disks](https://mlsanity.com/paper/2111.15196/) (2021)
- [Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification](https://mlsanity.com/paper/2201.01203/) (2022)

## Built on by (most cited first, 4 of 4)

- [Locating Hidden Exoplanets in ALMA Data Using Machine Learning](https://mlsanity.com/paper/2211.09541/) (2022)
- [GRINN: A Physics-Informed Neural Network for solving hydrodynamic systems in the presence of self-gravity](https://mlsanity.com/paper/2308.08010/) (2023)
- [Disk2Planet: A Robust and Automated Machine Learning Tool for Parameter Inference in Disk-Planet Systems](https://mlsanity.com/paper/2409.17228/) (2024)
- [VADER: A Variational Autoencoder to Infer Planetary Masses and Gas-Dust Disk Properties Around Young Stars](https://mlsanity.com/paper/2509.12324/) (2025)

## Abstract (arXiv)

Planet induced sub-structures, like annular gaps, observed in dust emission from protoplanetary disks provide a unique probe to characterize unseen young planets. While deep learning based model has an edge in characterizing the planet's properties over traditional methods, like customized simulations and empirical relations, it lacks in its ability to quantify the uncertainty associated with its predictions. In this paper, we introduce a Bayesian deep learning network "DPNNet-Bayesian" that can predict planet mass from disk gaps and provides uncertainties associated with the prediction. A unique feature of our approach is that it can distinguish between the uncertainty associated with the deep learning architecture and 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 textscfargo3d hydrodynamics code with a newly implemented fixed grain size module and improved initial conditions. The Bayesian framework enables estimating 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 dust gaps observed in HL Tau. The network predicts masses of  86.0 ± 5.5 MEarth ,  43.8 ± 3.3 MEarth , and  92.2 ± 5.1 MEarth  respectively, which are comparable to other studies based on specialized simulations.