用生成模型跨波段转换星系图像，填补观测空白。
Mapping Galaxy Images Across Ultraviolet, Visible and Infrared Bands Using Generative Deep Learning
- 基于模拟数据训练图像到图像的生成模型，实现多波段转换。
- 模型在真实数据（DECaLS）上验证有效，生成图像保真度高。
- 适合天文学家补全观测数据，优化望远镜观测规划。
我们证明了生成式深度学习可实现紫外、可见光与红外波段间星系图像的跨波段转换。利用Illustris模拟生成的虚拟观测数据，开发并验证了一个监督式图像到图像的模型，该模型可执行波段内插与外推。训练后的模型生成结果保真度高，通过通用图像指标（MAE、SSIM、PSNR）和天文学专用指标（GINI系数、M20）双重验证。此外，以DECaLS巡天数据为案例，展示了模型在真实观测中的预测能力。研究显示，生成式学习可扩充天文数据集，高效挖掘多波段信息，尤其适用于观测不完整的区域。本工作为优化任务规划、指导高分辨率后续观测、深化对星系形态与演化理解开辟新路径。
原文摘要 · Abstract (English)
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.
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