Galaxy image simplification using Generative AI

TL;DR

This paper presents a novel generative AI-based method for simplifying galaxy images into skeletonized forms, enabling shape analysis without pre-defined classes, demonstrated on 125,000 images from the DESI Legacy Survey.

Contribution

Introduces a new generative AI approach for galaxy image simplification and shape analysis that surpasses traditional classification methods.

Findings

Applied to 125,000 galaxy images from DESI Legacy Survey

Produced a publicly available catalog of simplified galaxy images

Enabled accurate shape measurements without pre-defined classes

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.