Using Bayesian Deep Learning to infer Planet Mass from Gaps in Protoplanetary Disks. (arXiv:2202.11730v1 [astro-ph.EP])

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 …

arxiv bayesian bayesian deep learning deep learning learning planet