2025/09/15 by Sayed Shafaat Mahmud, Mahmud, Sayed Shafaat, Sayantan Auddy +5
Engineering · Environmental Science · Physics and Astronomy · #Astro and Planetary Science #Earth and Planetary Astrophysics (astro-ph.EP) #FOS: Computer and information sciences #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine Learning (cs.LG) #Methane Hydrates and Related Phenomena #Space Exploration and Technology
paper · pdf · doi:10.48550/arxiv.2509.12324
openalex publication_date 2025/09/15 · openalex created_date 2025/10/18 · openalex updated_date 2026/07/28
We present 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 FARGO3D simulations post-processed with RADMC3D. Our trained model predicts physical planet and disk parameters with R2 > 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.