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Using Bayesian Deep Learning to Infer Planet Mass from Gaps in Protoplanetary Disks.
- Source :
-
Astrophysical Journal . 9/1/2022, Vol. 936 Issue 1, p1-11. 11p. - Publication Year :
- 2022
-
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 diskâ€"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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0004637X
- Volume :
- 936
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Astrophysical Journal
- Publication Type :
- Academic Journal
- Accession number :
- 159060633
- Full Text :
- https://doi.org/10.3847/1538-4357/ac7a3c