VADER: Variational Autoencoder for Disks with Embedded Rings

We present VADER, a Variational Autoencoder framework for inferring both planetary and global disk properties from high-resolution ALMA dust continuum images of protoplanetary disks. Unlike existing deterministic models, VADER performs uncertainty-aware inference of planet masses, α-viscosity, dust-to-gas ratio, Stokes number, flaring index, and planet multiplicity. Trained on over 100,000 synthetic images from FARGO3D simulations post-processed with RADMC-3D, the model reconstructs disk morphologies with SSIM > 0.99 and recovers physical parameters with R^2>0.9. 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.