Mapping Galaxy Images Across Ultraviolet, Visible and Infrared Bands Using Generative Deep Learning

We demonstrate that generative deep learning can translate galaxy observations across ultraviolet, visible, and infrared photometric bands. Leveraging mock observations from the Illustris simulations, we develop and validate a supervised image-to-image model capable of performing both band interpolation and extrapolation. The resulting trained models exhibit high fidelity in generating outputs, as verified by both general image comparison metrics (mean absolute error, SSIM, PSNR) and specialized astronomical metrics (Gini coefficient, M20). Moreover, we show that our models can be used to predict real-world observations, using data from the DECaLS survey as a case study. These findings highlight the potential of generative learning to augment astronomical data sets, enabling efficient exploration of multiband information in regions where observations are incomplete. This work opens new pathways for optimizing mission planning, guiding high-resolution follow-ups, and enhancing our understanding of galaxy morphology and evolution.