Kavli Affiliate: Ruobing Dong
| First 5 Authors: Shunyuan Mao, Ruobing Dong, Kwang Moo Yi, Lu Lu, Sifan Wang
| Summary:
We introduce Disk2Planet, a machine learning-based tool to infer key
parameters in disk-planet systems from observed protoplanetary disk structures.
Disk2Planet takes as input the disk structures in the form of two-dimensional
density and velocity maps, and outputs disk and planet properties, that is, the
Shakura–Sunyaev viscosity, the disk aspect ratio, the planet–star mass ratio,
and the planet’s radius and azimuth. We integrate the Covariance Matrix
Adaptation Evolution Strategy (CMA–ES), an evolutionary algorithm tailored for
complex optimization problems, and the Protoplanetary Disk Operator Network
(PPDONet), a neural network designed to predict solutions of disk–planet
interactions. Our tool is fully automated and can retrieve parameters in one
system in three minutes on an Nvidia A100 graphics processing unit. We
empirically demonstrate that our tool achieves percent-level or higher
accuracy, and is able to handle missing data and unknown levels of noise.
| Search Query: ArXiv Query: search_query=au:”Ruobing Dong”&id_list=&start=0&max_results=3