Mapping Galaxy Images Across Ultraviolet, Visible and Infrared Bands Using Generative Deep Learning
This work addresses the challenge of incomplete multi-band observations in astronomy, enabling dataset augmentation for mission planning and galaxy studies, though it is incremental as it applies existing generative methods to a new domain.
The authors tackled the problem of translating galaxy images across ultraviolet, visible, and infrared bands using generative deep learning, achieving high fidelity as verified by metrics like MAE, SSIM, PSNR, GINI coefficient, and M20, and demonstrated its application to real-world data from the DECaLS survey.
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.