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.

本研究针对盘-行星系统参数推断中的数据缺失和噪声问题，提出了Disk2Planet，这是一种基于机器学习的工具，能够自动化地从观测到的原行星盘结构中推断关键参数。研究表明，Disk2Planet可以在三分钟内提供高达百分之一的准确性，显著提高了盘-行星相互作用的理解和分析效率。

Disk2Planet：用于盘-行星系统参数推断的稳健自动化机器学习工具