Galactic Alchemy: Deep Learning Map-to-Map Translation in Hydrodynamical Simulations

Abstract We present the first systematic study of multi-domain map-to-map translation in galaxy formation simulations, leveraging deep generative models to predict diverse galactic properties. Using high-resolution magneto-hydrodynamical simulation data (from the IllustrisTNG suite), we compare conditional generative adversarial networks (GANs) and diffusion models under unified preprocessing and evaluation, optimizing their U-Net architectures and attention mechanisms for physical fidelity on galactic scales. Our approach jointly addresses seven astrophysical domains – including dark matter, gas, neutral hydrogen, stellar mass, temperature, and magnetic field strength – while introducing physics-aware evaluation metrics that quantify structural realism beyond standard computer vision measures. We demonstrate that translation difficulty correlates with physical coupling, achieving near-perfect fidelity for mappings from gas to dark matter and mappings involving astro-chemical components such as total gas to H i content, while identifying fundamental challenges in weakly constrained tasks such as gas to stellar mass mappings. Our results establish GAN-based models as competitive counterparts to state-of-the-art diffusion approaches at a fraction of the computational cost (in training and inference), paving the way for scalable, physics-aware generative frameworks for forward modelling and observational reconstruction in the SKA era.