Galactic alchemy: deep learning map-to-map translation in hydrodynamical simulations

ABSTRACT We present the first systematic study of multidomain map-to-map translation in galaxy formation simulations, leveraging deep generative models to predict diverse galactic properties. Using high-resolution magnetohydrodynamical simulation data (from the I llustris TNG suite), we compare conditional generative adversarial networks (GANs) and diffusion models under unified pre-processing 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 Square Kilometre Array (SKA) era.