Galaxy image simplification using Generative AI

2025-07-15

AI Summary

Modern astronomical surveys generate vast, morphologically diverse galaxy images, yet conventional classification methods are constrained by predefined categories and lack interpretability. Method: This paper proposes a generative AI–based galaxy image skeletonization framework that maps raw galaxy images to topology-preserving, semantically meaningful skeletal representations via an end-to-end deep generative model—without requiring prior morphological labels. Contribution/Results: Trained and evaluated on 125,000 images from the DESI survey, the framework produces the first publicly available large-scale galaxy skeletonization dataset. Experiments demonstrate that the skeletonized representations significantly improve measurement accuracy and robustness of key morphological parameters—including ellipticity and inclination—and enable unsupervised and open-ended morphological analysis. This work establishes a novel, interpretable, and generalizable paradigm for large-sample studies of galaxy structure.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Lexical Semantics and Morphology

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

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.

Problem

• Simplify galaxy images using generative AI
• Enable accurate shape measurements without predefined classes
• Automate analysis of large-scale galaxy survey data

Innovation

• Generative AI simplifies galaxy images
• Converts images to skeletonized form
• Enables analysis beyond predefined classes