用深度学习从射电图像推断行星质量和原行星盘属性
VADER: A Variational Autoencoder to Infer Planetary Masses and Gas-Dust Disk Properties Around Young Stars
- 基于变分自编码器，从尘埃连续谱图像中联合推断行星质量与盘参数
- 对10万张模拟图像训练后，预测相关系数超0.9，真实数据吻合度高
- 适合参与大巡天项目、研究原行星盘结构的天体物理学者使用
我们提出VADER（嵌环原行星盘变分自编码器），用于从高分辨率ALMA尘埃连续谱图像中同时推断行星质量与全局盘属性。VADER是一种概率深度学习模型，可直接从原行星盘图像中实现对行星质量、α-粘滞系数、尘气比、斯托克斯数、翘曲指数及行星数量的不确定性感知推断。模型在超过10万张由FARGO3D模拟并经RADMC3D后处理生成的合成图像上训练，对真实23个盘的预测结果与文献值一致，揭示了符合已知盘物理规律的隐含关联。实验表明，基于VAE的生成模型是进行概率天体物理推断的有力工具，适用于大规模干涉测量巡天时代下的原行星盘亚结构解析。
原文摘要 · Abstract (English)
We present \textbf{VADER} (Variational Autoencoder for Disks Embedded with Rings), for inferring both planet mass and global disk properties from high-resolution ALMA dust continuum images of protoplanetary disks (PPDs). VADER, a probabilistic deep learning model, enables uncertainty-aware inference of planet masses, $α$-viscosity, dust-to-gas ratio, Stokes number, flaring index, and the number of planets directly from protoplanetary disk images. VADER is trained on over 100{,}000 synthetic images of PPDs generated from \texttt{FARGO3D} simulations post-processed with \texttt{RADMC3D}. Our trained model predicts physical planet and disk parameters with $R^2 > 0.9$ from dust continuum images of PPDs. Applied to 23 real disks, VADER's mass estimates are consistent with literature values and reveal latent correlations that reflect known disk physics. Our results establish VAE-based generative models as robust tools for probabilistic astrophysical inference, with direct applications to interpreting protoplanetary disk substructures in the era of large interferometric surveys.
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