用生成式AI简化星系图像，实现无需预设类别的自动分析
Galaxy image simplification using Generative AI
- 基于生成式AI将星系图像转化为骨架化形式
- 处理12.5万张星系图像，生成可公开获取的简化图像目录
- 突破传统分类限制，适合大规模星系形态研究
现代数字巡天已获取数十亿个星系的图像。尽管这些图像通常足以分析星系形状，但对海量图像进行精确分析仍需有效自动化手段。现有方法多依赖机器学习对星系图像进行预定义类别的标注。本文提出一种基于生成式AI的新方法，可自动将星系图像简化为“骨架化”形式，从而实现不受限于预定义类别的准确形状测量与分析。该方法应用于DESI Legacy Survey的星系图像，共处理12.5万张图像，相关代码与数据集均已公开。
原文摘要 · Abstract (English)
Modern digital sky surveys have been acquiring images of billions of galaxies. While these images often provide sufficient details to analyze the shape of the galaxies, accurate analysis of such high volumes of images requires effective automation. Current solutions often rely on machine learning annotation of the galaxy images based on a set of pre-defined classes. Here we introduce a new approach to galaxy image analysis that is based on generative AI. The method simplifies the galaxy images and automatically converts them into a ``skeletonized" form. The simplified images allow accurate measurements of the galaxy shapes and analysis that is not limited to a certain pre-defined set of classes. We demonstrate the method by applying it to galaxy images acquired by the DESI Legacy Survey. The code and data are publicly available. The method was applied to 125,000 DESI Legacy Survey images, and the catalog of the simplified images is publicly available.
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