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

TL;DR

This paper presents a deep learning model that translates galaxy images across ultraviolet, visible, and infrared bands, enabling data augmentation and analysis of galaxy properties with high fidelity.

Contribution

It introduces a supervised generative model capable of band interpolation and extrapolation using simulated data, validated with real survey observations, advancing multi-band galaxy imaging techniques.

Findings

High-fidelity image translation across bands

Effective use of simulated and real data

Potential to augment astronomical datasets

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.