使用生成式AI简化星系图像

IF 1.9 4区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Astronomy and Computing Pub Date : 2025-07-17 DOI:10.1016/j.ascom.2025.100990

Sai Teja Erukude, Lior Shamir

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引用次数: 0

摘要

现代数字巡天已经获得了数十亿个星系的图像。虽然这些图像通常提供足够的细节来分析星系的形状，但对如此大量的图像进行准确分析需要有效的自动化。目前的解决方案通常依赖于基于一组预定义类的星系图像的机器学习注释。本文介绍了一种基于生成式人工智能的星系图像分析新方法。该方法简化了星系图像，并自动将其转换为“骨架”形式。简化的图像允许对星系形状的精确测量和分析，而不局限于特定的预定义类别。我们通过将其应用于由DESI遗产调查获得的星系图像来演示该方法。方法中使用的代码和数据是公开的。该方法应用于125,000张DESI Legacy Survey图像，简化图像的目录是公开的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Galaxy image simplification using Generative AI

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Galaxy image simplification using Generative AI

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 used in the method 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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来源期刊

Astronomy and Computing ASTRONOMY & ASTROPHYSICSCOMPUTER SCIENCE,-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

4.10

自引率

8.00%

发文量

期刊介绍： Astronomy and Computing is a peer-reviewed journal that focuses on the broad area between astronomy, computer science and information technology. The journal aims to publish the work of scientists and (software) engineers in all aspects of astronomical computing, including the collection, analysis, reduction, visualisation, preservation and dissemination of data, and the development of astronomical software and simulations. The journal covers applications for academic computer science techniques to astronomy, as well as novel applications of information technologies within astronomy.