Galaxy image simplification using Generative AI
This addresses the need for efficient and flexible automation in astronomy for analyzing billions of galaxy images, though it appears incremental as it builds on existing generative AI techniques for a specific domain.
The paper tackles the problem of automating galaxy image analysis from large-scale sky surveys by introducing a generative AI method that simplifies images into skeletonized forms, enabling accurate shape measurements without reliance on pre-defined classes, and demonstrates it on 125,000 DESI Legacy Survey images.
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.