用AI从星盘图像自动推断行星系统参数，三分钟出结果。
Disk2Planet: A Robust and Automated Machine Learning Tool for Parameter Inference in Disk-Planet Systems
- 结合进化算法与神经网络，自动反推星盘参数
- 精度达百分比级别，可处理缺失数据和噪声
- 适合天体物理研究者快速分析观测星盘
我们提出Disk2Planet，一种基于机器学习的工具，用于从原行星盘结构中推断关键参数。该工具输入二维密度和速度分布图，输出星盘黏度（Shakura--Sunyaev）、星盘高宽比、行星-恒星质量比、行星半径及方位角。通过集成协方差矩阵自适应进化策略（CMA--ES）与原行星盘算子网络（PPDONet），实现全自动化参数反演。在Nvidia A100上单系统推理仅需三分钟。实验表明，该方法精度达到百分比级别以上，且能有效应对缺失数据与未知噪声水平。
原文摘要 · Abstract (English)
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.
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