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

ABSTRACT 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 (MAE, SSIM, PSNR) and specialized astronomical metrics (GINI coefficient, M20). Moreover, we show that our model 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 datasets, enabling efficient exploration of multi-band 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. INTRODUCTION Galaxies can appear quite different when observed across various photometric bands due to the wavelength-dependent nature of the light they emit (Kouroumpatzakis et al. 2023). In shorter wavelength bands, like the ultraviolet (U) band, galaxies reveal the presence of hot, young stars and regions of active star formation. Meanwhile, in optical bands (e.g., G, R), the light primarily comes from older, cooler stars. At longer wavelengths, such as in infrared bands, the emission is dominated by dust and the cooler, more evolved stellar populations. Studying galaxies across multiple photometric bands is essential because each band reveals different aspects of a galaxy's physical properties, offering a more complete picture of its structure, composition, and evolution. By combining observations from various bands, it is possible to trace a galaxy's star formation history, stellar populations, dust content, and even interactions with its environment. Observations made in multiple wavelengths require fewer inferences and lead to stronger conclusions being drawn about the observed galaxies.