# Galaxy image simplification using Generative AI

arXiv: https://arxiv.org/abs/2507.11692 · HTML: https://mlsanity.com/paper/2507.11692/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 72 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Pros and Cons of GAN Evaluation Measures: New Developments](https://mlsanity.com/paper/2103.09396/) (2021)
- [Explaining in Style: Training a GAN to explain a classifier in StyleSpace](https://mlsanity.com/paper/2104.13369/) (2021)
- [Rotation-invariant convolutional neural networks for galaxy morphology prediction](https://mlsanity.com/paper/1503.07077/) (2015)
- [How good is my GAN?](https://mlsanity.com/paper/1807.09499/) (2018)
- [Patch-Based Image Inpainting with Generative Adversarial Networks](https://mlsanity.com/paper/1803.07422/) (2018)
- [Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies](https://mlsanity.com/paper/2102.08414/) (2021)
- [Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN](https://mlsanity.com/paper/1706.03319/) (2017)
- [Identifying Bias in Deep Neural Networks Using Image Transforms](https://mlsanity.com/paper/2412.13079/) (2024)
- [Combining human and machine learning for morphological analysis of galaxy images](https://mlsanity.com/paper/1409.7935/) (2014)
- [A catalog of broad morphology of Pan-STARRS galaxies based on deep learning](https://mlsanity.com/paper/2010.06073/) (2020)
- [Systematic biases when using deep neural networks for annotating large catalogs of astronomical images](https://mlsanity.com/paper/2201.03131/) (2022)
- [Rotation Invariant Descriptors for Galaxy Morphological Classification](https://mlsanity.com/paper/1812.04706/) (2018)
- [Automatic identification of outliers in Hubble Space Telescope galaxy images](https://mlsanity.com/paper/2101.02623/) (2021)
- [Galaxy Image Classification using Hierarchical Data Learning with Weighted Sampling and Label Smoothing](https://mlsanity.com/paper/2212.10081/) (2022)
- [Outlier galaxy images in the Dark Energy Survey and their identification with unsupervised machine learning](https://mlsanity.com/paper/2305.01720/) (2023)

## Built on by (most cited first, 1 of 1)

- [Amplifying the imaging power of digital sky surveys with space telescopes data and generative AI](https://mlsanity.com/paper/2608.20666/) (2026)

## Abstract (arXiv)

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.